Search results for "Machine Learning for Pattern Recognition"
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Machine Learning and Knowledge Discovery in Databases
This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2008, held in Antwerp, Belgium, in September 2008. The 100 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 521 submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. The topics addressed are application of machine learning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques.
R 3,107.28
Artificial Intelligence: Towards Sustainable Intelligence
This book constitutes the proceedings of the Second International Conference on Artificial Intelligence: Towards Sustainable Intelligence, AI4S 2024, held in Alcala de Henares, Spain, during October 3-4, 2024. The 16 full papers and 2 short papers included in this book were carefully reviewed and selected from 59 submissions. They deal with trustworthy AI and related topics, focusing on software and its engineering; software development process management and methods, etc.
R 3,121.39
R 4,558.00
Alternating Direction Method of Multipliers for Machine Learning
Machine learning heavily relies on optimization algorithms to solve its learning models. Constrained problems constitute a major type of optimization problem, and the alternating direction method of multipliers (ADMM) is a commonly used algorithm to solve constrained problems, especially linearly constrained ones. Written by experts in machine learning and optimization, this is the first book providing a state-of-the-art review on ADMM under various scenarios, including deterministic and convex optimization, nonconvex optimization, stochastic optimization, and distributed optimization. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference book for users who are seeking a relatively universal algorithm for constrained problems. Graduate students or researchers can read it to grasp the frontiers of ADMM in machine learning in a short period of time.
R 5,861.00
Graphics Recognition: Algorithms and Systems
This book constitutes the strictly refereed post-workshop proceedings of the Second International Workshop on Graphics Recognition, GREC'97, held in Nancy, France, in August 1997. The 34 thoroughly revised full papers presented were carefully selected for inclusion in the book on the basis of a second round of post-workshop reviewing. The book is divided into sections on vectorization and segmentation, symbol recognition, form processing, map processing, engineering drawings, applications and systems, performance evaluation, and a graphics recognition contest.
R 1,578.84
Machine Learning for Advanced Functional Materials
This book presents recent advancements of machine learning methods and their applications in material science and nanotechnologies. It provides an introduction to the field and for those who wish to explore machine learning in modeling as well as conduct data analyses of material characteristics. The book discusses ways to enhance the material’s electrical and mechanical properties based on available regression methods for supervised learning and optimization of material attributes. In summary, the growing interest among academics and professionals in the field of machine learning methods in functional nanomaterials such as sensors, solar cells, and photocatalysis is the driving force for behind this book. This is a comprehensive scientific reference book on machine learning for advanced functional materials and provides an in-depth examination of recent achievements in material science by focusing on topical issues using machine learning methods.
R 6,594.00
Design and Analysis of Learning Classifier Systems
This book is probably best summarized as providing a principled foundation for Learning Classi?er Systems. Something is happening in LCS, and particularly XCS and its variants that clearly often produces good results. Jan Drug- itsch wishes to understand this from a broader machine learning perspective and thereby perhaps to improve the systems. His approach centers on choosing a statistical de?nition – derived from machine learning – of “a good set of cl- si?ers”, based on a model according to which such a set represents the data. For an illustration of this approach, he designs the model to be close to XCS, and tests it by evolving a set of classi?ers using that de?nition as a ?tness criterion, seeing ifthe setprovidesa goodsolutionto twodi?erent function approximation problems. It appears to, meaning that in some sense his de?nition of “good set of classi?ers” (also, in his terms, a good model structure) captures the essence, in machine learning terms, of what XCS is doing. In the process of designing the model, the author describes its components and their training in clear detail and links it to currently used LCS, giving rise to recommendations for how those LCS can directly gain from the design of the model and its probabilistic formulation. The seeming complexity of evaluating the quality ofa set ofclassi?ersis alleviatedby giving analgorithmicdescription of how to do it, which is carried out via a simple Pittsburgh-style LCS.
R 4,030.00
Speech Perception and Spoken Word Recognition
Speech Perception and Spoken Word Recognition features contributions from the field’s leading scientists, and covers recent developments and current issues in the study of cognitive and neural mechanisms that take patterns of air vibrations and turn them ‘magically’ into meaning. The volume makes a unique theoretical contribution in linking behavioural and cognitive neuroscience research, and cutting across traditional strands of study, such as adult and developmental processing.The book: Focusses on the state of the art in the study of speech perception and spoken word recognition Discusses the interplay between behavioural and cognitive neuroscience evidence, and between adult and developmental research Evaluates key theories in the field and relates them to recent empirical advances, including the relationship between speech perception and speech production, meaning representation and real-time activation, and bilingual and monolingual spoken word recognition Examines emerging areas of study such as word learning and time-course of memory consolidation, and how the science of human speech perception can help computer speech recognitionOverall this book presents a renewed focus on theoretical and developmental issues, as well as a multifaceted and broad review of the state of research, in speech perception and spoken word recognition. Particularly interested readers will be researchers of psycholinguistics and adjoining fields as well as advanced undergraduate and postgraduate students.
R 3,340.92
Processing, Analyzing and Learning of Images, Shapes, and Forms: Part 2
Processing, Analyzing and Learning of Images, Shapes, and Forms: Part 2, Volume 20, surveys the contemporary developments relating to the analysis and learning of images, shapes and forms, covering mathematical models and quick computational techniques. Chapter cover Alternating Diffusion: A Geometric Approach for Sensor Fusion, Generating Structured TV-based Priors and Associated Primal-dual Methods, Graph-based Optimization Approaches for Machine Learning, Uncertainty Quantification and Networks, Extrinsic Shape Analysis from Boundary Representations, Efficient Numerical Methods for Gradient Flows and Phase-field Models, Recent Advances in Denoising of Manifold-Valued Images, Optimal Registration of Images, Surfaces and Shapes, and much more.
R 6,768.00
Human Recognition at a Distance in Video
Most biometric systems employed for human recognition require physical contact with, or close proximity to, a cooperative subject. Far more challenging is the ability to reliably recognize individuals at a distance, when viewed from an arbitrary angle under real-world environmental conditions. Gait and face data are the two biometrics that can be most easily captured from a distance using a video camera.This comprehensive and logically organized text/reference addresses the fundamental problems associated with gait and face-based human recognition, from color and infrared video data that are acquired from a distance. It examines both model-free and model-based approaches to gait-based human recognition, including newly developed techniques where the both the model and the data (obtained from multiple cameras) are in 3D. In addition, the work considers new video-based techniques for face profile recognition, and for the super-resolution of facial imagery obtained at different angles. Finally, the book investigates integrated systems that detect and fuse both gait and face biometrics from video data. Topics and features: discusses a framework for human gait analysis based on Gait Energy Image, a spatio-temporal gait representation; evaluates the discriminating power of model-based gait features using Bayesian statistical analysis; examines methods for human recognition using 3D gait biometrics, and for moving-human detection using both color and thermal image sequences; describes approaches for the integration face profile and gait biometrics, and for super-resolution of frontal and side-view face images; introduces an objective non-reference quality evaluation algorithm for super-resolved images; presents performance comparisons between different biometrics and different fusion methods for integrating gait and super-resolved face from video.This unique and authoritative text is an invaluable resource for researchers and graduate students of computer vision, pattern recognition and biometrics. The book will also be of great interest to professional engineers of biometric systems.
R 3,627.18
Artificial Neural Networks and Machine Learning – ICANN 2026
The five-volume set LNCS 17089-17093 constitutes the proceedings of the 35th International Conference on Artificial Neural Networks and Machine Learning, ICANN 2026, held in Padua, Italy on September 14–17, 2026. The 296 papers included in these conference proceedings were carefully reviewed and selected from 450 submissions. The conference strongly values the synergy between theoretical progress and impactful real-world applications, and actively encourages contributions that demonstrate how artificial neural networks are being used to address pressing societal and technological challenges.
R 3,590.18
Learning and Intelligent Optimization
This book constitutes the thoroughly refereed post-conference proceedings of the 11th International Conference on Learning and Intelligent Optimization, LION 11, held in Nizhny,Novgorod, Russia, in June 2017. The 20 full papers (among these one GENOPT paper) and 15 short papers presented have been carefully reviewed and selected from 73 submissions. The papers explore the advanced research developments in such interconnected fields as mathematical programming, global optimization, machine learning, and artificial intelligence. Special focus is given to advanced ideas, technologies, methods, and applications in optimization and machine learning.
R 2,190.63
Errant Intelligence
Artificial intelligence is often framed as a quest to replicate the human brain, promising frictionless cognition and a future of seamless automation. But what if this pervasive narrative obscures a deeper, more ‘errant’ truth?Errant Intelligence challenges the prevailing biological and individualistic interpretations of machine learning, arguing instead for a radical understanding of machine intelligence. The book embraces the deviations, inconsistencies, and ‘errant behaviour’ as fundamental to the discovery of new knowledge, moving beyond the illusion of mere optimisation. Drawing on media theory, cybernetics, and a unique psychoanalytic lens, it explores the ‘technological unconscious’ of machine learning. It traces the historical roots of AI, from early automatons and the Turing machine to natural language processing and contemporary machine learning systems. Challenging the idea of an autonomous, self-generating AI, the book exposes the hidden labour, assumed logics, and inherent biases that animate its operation. It re-evaluates computational thinking, insisting on its inherently social, collective, and symbolic character, and revealing how language and logical paradoxes are not obstacles, but constitutive forces that shape intelligent machines.Errant Intelligence offers a vital new framework for understanding the profound co-evolution of human and machine learning. It’s time to ‘unlearn’ our assumptions and embrace the productive ambiguity and fallibility at the core of machine intelligence.
R 8,572.00
Machine Learning and Knowledge Discovery in Databases
This three-volume set LNAI 8188, 8189 and 8190 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2013, held in Prague, Czech Republic, in September 2013. The 111 revised research papers presented together with 5 invited talks were carefully reviewed and selected from 447 submissions. The papers are organized in topical sections on reinforcement learning; Markov decision processes; active learning and optimization; learning from sequences; time series and spatio-temporal data; data streams; graphs and networks; social network analysis; natural language processing and information extraction; ranking and recommender systems; matrix and tensor analysis; structured output prediction, multi-label and multi-task learning; transfer learning; bayesian learning; graphical models; nearest-neighbor methods; ensembles; statistical learning; semi-supervised learning; unsupervised learning; subgroup discovery, outlier detection and anomaly detection; privacy and security; evaluation; applications; and medical applications.
R 1,628.82
Generative Adversarial Learning: Architectures and Applications
This book provides a collection of recent research works addressing theoretical issues on improving the learning process and the generalization of GANs as well as state-of-the-art applications of GANs to various domains of real life. Adversarial learning fascinates the attention of machine learning communities across the world in recent years. Generative adversarial networks (GANs), as the main method of adversarial learning, achieve great success and popularity by exploiting a minimax learning concept, in which two networks compete with each other during the learning process. Their key capability is to generate new data and replicate available data distributions, which are needed in many practical applications, particularly in computer vision and signal processing. The book is intended for academics, practitioners, and research students in artificial intelligence looking to stay up to date with the latest advancements on GANs’ theoretical developments and their applications.
R 6,261.60
Visual Object Recognition
The visual recognition problem is central to computer vision research. From robotics to information retrieval, many desired applications demand the ability to identify and localize categories, places, and objects. This tutorial overviews computer vision algorithms for visual object recognition and image classification. We introduce primary representations and learning approaches, with an emphasis on recent advances in the field. The target audience consists of researchers or students working in AI, robotics, or vision who would like to understand what methods and representations are available for these problems. This lecture summarizes what is and isn't possible to do reliably today, and overviews key concepts that could be employed in systems requiring visual categorization. Table of Contents: Introduction / Overview: Recognition of Specific Objects / Local Features: Detection and Description / Matching Local Features / Geometric Verification of Matched Features / Example Systems: Specific-Object Recognition / Overview: Recognition of Generic Object Categories / Representations for Object Categories / Generic Object Detection: Finding and Scoring Candidates / Learning Generic Object Category Models / Example Systems: Generic Object Recognition / Other Considerations and Current Challenges / Conclusions
R 1,634.96
Geometry Fundamentals of Computer Vision
This book enables readers to acquire a fundamental knowledge of computer vision from the perspective of geometry, including knowledge of image processing and pattern recognition intended for two-dimensional geometry analysis of images, knowledge of computer vision intended for three-dimensional geometry analysis of images. From the pedagogic point of view, the author intends that this book helps students develop an ability to flexibly apply geometry fundamentals of computer vision to solve practical engineering problems. In this sense, this book is also a professional reference for engineers dedicated to computer vision involved intelligent systems. This book attaches importance to clarification of how relevant knowledge of computer vision stems from practical applications and emphasizes the dialectic relationship between the knowledge and practical applications, enabling readers not only to “know how” for practice, but also to “know why” in terms of mathematical essence. Throughout this book, the author tends to provide detailed theoretical derivations and explanations to clarify essential reasons behind computer vision methods. Besides, this book provides plenty of original demonstration code scripts (in Matlab) that are complete, interesting, easy for practice, and of application values for engineering activities. By code demonstration, the author presents how to flexibly take advantage of geometry fundamentals of computer vision to realize various kinds of visual effects that are actually technology basis of many interesting and useful applications.
R 10,715.90
Machine Learning for Health Informatics
Machine learning (ML) is the fastest growing field in computer science, and Health Informatics (HI) is amongst the greatest application challenges, providing future benefits in improved medical diagnoses, disease analyses, and pharmaceutical development. However, successful ML for HI needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to visualization. Tackling complex challenges needs both disciplinary excellence and cross-disciplinary networking without any boundaries. Following the HCI-KDD approach, in combining the best of two worlds, it is aimed to support human intelligence with machine intelligence. This state-of-the-art survey is an output of the international HCI-KDD expert network and features 22 carefully selected and peer-reviewed chapters on hot topics in machine learning for health informatics; they discuss open problems and future challenges in order to stimulate further research and international progress in this field.
R 2,262.78
Algorithmic Learning Theory
This book constitutes the refereed proceedings of the 17th International Conference on Algorithmic Learning Theory, ALT 2006, held in Barcelona, Spain in October 2006, colocated with the 9th International Conference on Discovery Science, DS 2006.The 24 revised full papers presented together with the abstracts of five invited papers were carefully reviewed and selected from 53 submissions. The papers are dedicated to the theoretical foundations of machine learning.
R 1,574.14
Advanced Intelligent Computing Technology and Applications
This 9-volume set CCIS 3034-3042 constitutes - in conjunction with the 21-volume set LNCS 16642-16662, the 5-volume set LNAI 16663-16667 and the 4-volume set LNBI 16668-16671 - the refereed proceedings of the 22nd International Conference on Intelligent Computing, ICIC 2026, held in ON, Canada, July 22–26, 2026.The 336 papers presented in these proceedings books were carefully reviewed and selected from 4561 submissions.These papers have been organized in the following topical sections:Intelligent Computing in Computer VisionInformation SecurityImage ProcessingIntelligent Data Analysis amp PredictionIntelligent Image Document RetrievalsNeural NetworksPattern RecognitionSwarm Intelligence and OptimizationIntelligent Agent and Web ApplicationsIntelligent Computing in Communication NetworksMachine LearningSignal ProcessingAdvances in Multimodal Intelligence for Visual and Medical DataCognitive Intelligence Computing: Theories, Architectures, and Applications in the Era of Large ModelsIntelligent Data FusionMultimodal information processing and fusion with large language modelsNeural Signals and Intelligent Computing: From Brain Data to Trustworthy Human-AI CollaborationIntelligent Computing in Finance BankingIntelligent Control and AutomationReinforcement LearningSustainable Intelligent Computing: Efficient Systems, Algorithms, and Applications
R 2,583.47
Data Mining and Machine Learning in Building Energy Analysis
The energy consumption of a building has, in recent years, become a determining factor during its design and construction. With carbon footprints being a growing issue, it is important that buildings be optimized for energy conservation and CO2 reduction. This book therefore presents AI models and optimization techniques related to this application. The authors start with a review of recent models for the prediction of building energy consumption: engineering methods, statistical methods, artificial intelligence methods, ANNs and SVMs in particular. The book then focuses on SVMs, by first applying them to building energy consumption, then presenting the principles and various extensions, and SVR. The authors then move on to RDP, which they use to determine building energy faults through simulation experiments before presenting SVR model reduction methods and the benefits of parallel computing. The book then closes by presenting some of the current research and advancements in the field.
R 5,795.00
Partitional Clustering via Nonsmooth Optimization
This updated book describes optimization models of clustering problems and clustering algorithms based on optimization techniques, including their implementation, evaluation, and applications. The book gives a comprehensive and detailed description of optimization approaches for solving clustering problems; the authors' emphasis on clustering algorithms is based on deterministic methods of optimization. The book also includes results on real-time clustering algorithms based on optimization techniques, addresses implementation issues of these clustering algorithms, and discusses new challenges arising from very large data and data with noise and outliers. The book is ideal for anyone teaching or learning clustering algorithms. It provides an accessible introduction to the field and it is well suited for practitioners already familiar with the basics of optimization.
R 5,861.00
Applications of Deep Learning in Electromagnetics
Deep learning has started to be applied to solving many electromagnetic problems, including the development of fast modelling solvers, accurate imaging algorithms, efficient design tools for antennas, as well as tools for wireless links/channels characterization. The contents of this book represent pioneer applications of deep learning techniques to electromagnetic engineering, where physical principles described by the Maxwell's equations dominate. With the development of deep learning techniques, improvement in learning capacity and generalization ability may allow machines to "learn" from properly collected data and "master" the physical laws in certain controlled boundary conditions. In the long run, a hybridization of fundamental physical principles with knowledge from training data could unleash numerous possibilities in electromagnetic theory and engineering that used to be impossible due to the limit of data information and ability of computation. Electromagnetic applications of deep learning covered in the book include electromagnetic forward modeling, free-space inverse scattering, non-destructive testing and evaluation, subsurface imaging, biomedical imaging, direction of arrival estimation, remote sensing, digital satellite communications, imaging and gesture recognition, metamaterials and metasurfaces design, as well as microwave circuit modeling. Applications of Deep Learning in Electromagnetics contains valuable information for researchers looking for new tools to solve Maxwell's equations, students of electromagnetic theory, and researchers in the field of deep learning with an interest in novel applications.
R 4,702.00
Hardware-Aware Probabilistic Machine Learning Models
This book proposes probabilistic machine learning models that represent the hardware properties of the device hosting them. These models can be used to evaluate the impact that a specific device configuration may have on resource consumption and performance of the machine learning task, with the overarching goal of balancing the two optimally. The book first motivates extreme-edge computing in the context of the Internet of Things (IoT) paradigm. Then, it briefly reviews the steps involved in the execution of a machine learning task and identifies the implications associated with implementing this type of workload in resource-constrained devices. The core of this book focuses on augmenting and exploiting the properties of Bayesian Networks and Probabilistic Circuits in order to endow them with hardware-awareness. The proposed models can encode the properties of various device sub-systems that are typically not considered by other resource-aware strategies, bringing about resource-saving opportunities that traditional approaches fail to uncover.The performance of the proposed models and strategies is empirically evaluated for several use cases. All of the considered examples show the potential of attaining significant resource-saving opportunities with minimal accuracy losses at application time. Overall, this book constitutes a novel approach to hardware-algorithm co-optimization that further bridges the fields of Machine Learning and Electrical Engineering.
R 2,851.17
Proceedings of 17th International Conference on Machine Learning and Computing
This book comprises original and peer reviewed research papers presented at 2025 17th International Conference on Machine Learning and Computing that was held in Guangzhou, China, from February 14 to 17, 2025. The focus of the conference is to establish an effective platform for institutions and industries to share ideas and to present the works of scientists, engineers, educators and students from all over the world. Topics discussed in this volume include Machine Learning Theory and Algorithms, High-performance Computing Models and Data Processing, Large-scale Language Models and Natural Language Processing, Data-oriented Information System Optimization and Intelligent Computing, AI-based Intelligent Control Systems and System Security, etc. The book will become a valuable resource for academics, industry professionals, and engineers working in the related fields of machine learning and computing.
R 8,059.00
Multiview Machine Learning
This book provides a unique, in-depth discussion of multiview learning, one of the fastest developing branches in machine learning. Multiview Learning has been proved to have good theoretical underpinnings and great practical success. This book describes the models and algorithms of multiview learning in real data analysis. Incorporating multiple views to improve the generalization performance, multiview learning is also known as data fusion or data integration from multiple feature sets. This self-contained book is applicable for multi-modal learning research, and requires minimal prior knowledge of the basic concepts in the field. It is also a valuable reference resource for researchers working in the field of machine learning and also those in various application domains.
R 6,496.44
R 1,667.55
Artificial Neural Networks and Machine Learning – ICANN 2022
The 4-volumes set of LNCS 13529, 13530, 13531, and 13532 constitutes the proceedings of the 31st International Conference on Artificial Neural Networks, ICANN 2022, held in Bristol, UK, in September 2022. The total of 255 full papers presented in these proceedings was carefully reviewed and selected from 561 submissions. ICANN 2022 is a dual-track conference featuring tracks in brain inspired computing and machine learning and artificial neural networks, with strong cross-disciplinary interactions and applications.
R 2,257.64
Parallelism, Learning, Evolution
This volume presents the proceedings of a workshop onevolutionary models and strategies and another workshop onparallel processing, logic, organization, and technology,both held in Germany in 1989.In the search for new concepts relevant for parallel anddistributed processing, the workshop on parallel processingincluded papers on aspects of space and time,representations of systems, non-Boolean logics, metrics,dynamics and structure, and superposition and uncertainties.The point was stressed that distributed representations ofinformation may share features with quantum physics, such asthe superposition principle and the uncertainty relations.Much of the volume contains material on general parallelprocessing machines, neural networks, and system-theoreticaspects.The material on evolutionary strategies is included becausethese strategies will yield important and powerfulapplications for parallel processing machines, and open thewayto new problem classes to be treated by computers.
R 1,780.45
Machine Learning, Optimization, and Data Science
This book constitutes the post-conference proceedings of the 5th International Conference on Machine Learning, Optimization, and Data Science, LOD 2019, held in Siena, Italy, in September 2019. The 54 full papers presented were carefully reviewed and selected from 158 submissions. The papers cover topics in the field of machine learning, artificial intelligence, reinforcement learning, computational optimization and data science presenting a substantial array of ideas, technologies, algorithms, methods and applications.
R 2,254.41
AI and ML Techniques in IoT-based Communication
An insightful and up-to-date discussion of how to use contemporary IoT and AI technology to advance sustainable development initiatives In AI and ML Techniques in IoT-based Communication: A Path to Sustainable Development Goals, a team of distinguished editors presents an astute discussion of the importance of connected devices and intelligent algorithms in the generation of innovative solutions to global challenges. The book explains how to exploit the synergy between Internet of Things (IoT) and artificial intelligence (AI) technologies to further the United Nations’ Sustainable Development Goals (SDGs). Readers will learn how to enable smarter, data-driven approaches to difficult global problems, including climate change, inadequate health care delivery, and energy inefficiency. Expert contributors explore emerging trends, potential pitfalls, and likely future developments in IoT, AI, and machine learning (ML) in sustainable development. Additional topics include: A thorough introduction to IoT communication protocols and standards relevant to sustainable development Comprehensive explorations of artificial intelligence and machine learning techniques for enhancing IoT-based communication Practical discussions of water resource and energy management, sustainable agriculture, and sustainable transportation and logistics Complete treatments of AI-driven communication optimization for sustainability Perfect for academics, researchers, industry practitioners, and policymakers with an interest in IoT, AI, ML, and their applications to sustainable development, AI and ML Techniques in IoT-based Communication will also benefit professionals working at non-profit and non-governmental organizations focused on advancing sustainable development initiatives.
R 4,559.00
Nature-Inspired Computation and Machine Learning
The two-volume set LNAI 8856 and LNAI 8857 constitutes the proceedings of the 13th Mexican International Conference on Artificial Intelligence, MICAI 2014, held in Tuxtla, Mexico, in November 2014. The total of 87 papers plus 1 invited talk presented in these proceedings were carefully reviewed and selected from 348 submissions. The first volume deals with advances in human-inspired computing and its applications. It contains 44 papers structured into seven sections: natural language processing, natural language processing applications, opinion mining, sentiment analysis, and social network applications, computer vision, image processing, logic, reasoning, and multi-agent systems, and intelligent tutoring systems. The second volume deals with advances in nature-inspired computation and machine learning and contains also 44 papers structured into eight sections: genetic and evolutionary algorithms, neural networks, machine learning, machine learning applications to audio and text, data mining, fuzzy logic, robotics, planning, and scheduling, and biomedical applications.
R 1,597.36
Natural Object Recognition
Natural Object Recognition presents a totally new approach to the automation of scene understanding. Rather than attempting to construct highly specialized algorithms for recognizing physical objects, as is customary in modern computer vision research, the application and subsequent evaluation of large numbers of relatively straightforward image processing routines is used to recognize natural features such as trees, bushes, and rocks. The use of contextual information is the key to simplifying the problem to the extent that well understood algorithms give reliable results in ground-level, outdoor scenes.
R 1,539.34
Medical Computer Vision: Recognition Techniques and Applications in Medical Imaging
This book constitutes the thoroughly refereed workshop proceedings of the Second International Workshop on Medical Computer Vision, MCV 2012, held in Nice, France, October 2012 in conjunction with the 15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012. The 24 papers have been selected out of 42 submissions. At MCV 2012, 12 papers were presented as a poster and 12 as a poster together with a plenary talk. The book also features four selected papers which were presented at the previous CVPR Medical Computer Vision workshop held in conjunction with the International Conference on Computer Vision and Pattern Recognition on June 21 2012 in Providence, Rhode Island, USA. The papers explore the use of modern computer vision technology in tasks such as automatic segmentation and registration, localization of anatomical features and detection of anomalies, as well as 3D reconstruction and biophysical model personalization.
R 2,175.08
Low Field Pediatric Brain Magnetic Resonance Image Segmentation and Quality Assurance
This open access LNCS volume 15515 constitutes the refereed proceedings of the First MICCAI Challenge on Low Field Pediatric Brain Magnetic Resonance Image Segmentation and Quality Assurance, LISA 2024, Held in Conjunction with MICCAI 2024, in Marrakesh, Morocco, in October 2024.The 6 full papers presented were carefully reviewed and selected from 8 submissions. This MICCAI Challenge focuses on the development and evaluation of automatic image analysis and machine learning algorithms and Ultra-low-field brain imaging has the potential to become a transformative tool for both clinical and research applications.
R 2,127.85
Machine Learning in Cyber Trust
Many networked computer systems are far too vulnerable to cyber attacks that can inhibit their functioning, corrupt important data, or expose private information. Not surprisingly, the field of cyber-based systems is a fertile ground where many tasks can be formulated as learning problems and approached in terms of machine learning algorithms.This book contains original materials by leading researchers in the area and covers applications of different machine learning methods in the reliability, security, performance, and privacy issues of cyber space. It enables readers to discover what types of learning methods are at their disposal, summarizing the state-of-the-practice in this significant area, and giving a classification of existing work.Those working in the field of cyber-based systems, including industrial managers, researchers, engineers, and graduate and senior undergraduate students will find this an indispensable guide in creating systems resistant to and tolerant of cyber attacks.
R 6,228.00
Optimization and Learning
This book constitutes the refereed proceedings of the 7th International Conference on Optimization and Learning, OLA 2024, held in Dubrovnik, Croatia, during May 13–15, 2024.The 24 full papers presented here were carefully reviewed and selected from 64 submissions. They were organized in the following topical sections: synergies between optimization and machine learning; enhancing optimization and learning techniques; transportation and routing; and applications.
R 3,324.87
Information Theoretic Learning
This bookisan outgrowthoften yearsof researchatthe Universityof Florida Computational NeuroEngineering Laboratory (CNEL) in the general area of statistical signal processing and machine learning. One of the goals of writing the book is exactly to bridge the two ?elds that share so many common problems and techniques but are not yet e?ectively collaborating. Unlikeotherbooks thatcoverthe state ofthe artinagiven?eld,this book cuts across engineering (signal processing) and statistics (machine learning) withacommontheme:learningseenfromthepointofviewofinformationt- orywithanemphasisonRenyi'sde?nitionofinformation.Thebasicapproach is to utilize the information theory descriptors of entropy and divergence as nonparametric cost functions for the design of adaptive systems in unsup- vised or supervised training modes. Hence the title: Information-Theoretic Learning (ITL). In the course of these studies, we discovered that the main idea enabling a synergistic view as well as algorithmic implementations, does not involve the conventional central moments of the data (mean and covariance). Rather, the core concept is the ?-norm of the PDF, in part- ular its expected value (?= 2), which we call the information potential. This operator and related nonparametric estimators link information theory, optimization of adaptive systems, and reproducing kernel Hilbert spaces in a simple and unconventional way.
R 5,503.06
Application of Soft Computing, Machine Learning, Deep Learning and Optimizations in Geoengineering and Geoscience
This book summarizes the application of soft computing techniques, machine learning approaches, deep learning algorithms and optimization techniques in geoengineering including tunnelling, excavation, pipelines, etc. and geoscience including the geohazards, rock and soil properties, etc. The book features state-of-the-art studies on use of SC,ML,DL and optimizations in Geoengineering and Geoscience. Considering these points and understanding, this book will be compiled with highly focussed chapters that will discuss the application of SC,ML,DL and optimizations in Geoengineering and Geoscience. Target audience: (1) Students of UG, PG, and Research Scholars: Several applications of SC,ML,DL and optimizations in Geoengineering and Geoscience can help students to enhance their knowledge in this domain. (2) Industry Personnel and Practitioner: Practitioners from different fields can be able to implement standard and advanced SC,ML,DL and optimizations for solving critical problems of civil engineering.
R 7,421.89
Domain-Specific Computer Architectures for Emerging Applications
With the end of Moore’s Law, domain-specific architecture (DSA) has become a crucial mode of implementing future computing architectures. This book discusses the system-level design methodology of DSAs and their applications, providing a unified design process that guarantees functionality, performance, energy efficiency, and real-time responsiveness for the target application.DSAs often start from domain-specific algorithms or applications, analyzing the characteristics of algorithmic applications, such as computation, memory access, and communication, and proposing the heterogeneous accelerator architecture suitable for that particular application. This book places particular focus on accelerator hardware platforms and distributed systems for various novel applications, such as machine learning, data mining, neural networks, and graph algorithms, and also covers RISC-V open-source instruction sets. It briefly describes the system design methodology based on DSAs and presents the latest research results in academia around domain-specific acceleration architectures.Providing cutting-edge discussion of big data and artificial intelligence scenarios in contemporary industry and typical DSA applications, this book appeals to industry professionals as well as academicians researching the future of computing in these areas.
R 2,674.00
Advanced Intelligent Computing Technology and Applications
This 9-volume set CCIS 3034-3042 constitutes - in conjunction with the 21-volume set LNCS 16642-16662, the 5-volume set LNAI 16663-16667 and the 4-volume set LNBI 16668-16671 - the refereed proceedings of the 22nd International Conference on Intelligent Computing, ICIC 2026, held in ON, Canada, July 22–26, 2026.The 336 papers presented in these proceedings books were carefully reviewed and selected from 4561 submissions.These papers have been organized in the following topical sections:Intelligent Computing in Computer VisionInformation SecurityImage ProcessingIntelligent Data Analysis amp PredictionIntelligent Image Document RetrievalsNeural NetworksPattern RecognitionSwarm Intelligence and OptimizationIntelligent Agent and Web ApplicationsIntelligent Computing in Communication NetworksMachine LearningSignal ProcessingAdvances in Multimodal Intelligence for Visual and Medical DataCognitive Intelligence Computing: Theories, Architectures, and Applications in the Era of Large ModelsIntelligent Data FusionMultimodal information processing and fusion with large language modelsNeural Signals and Intelligent Computing: From Brain Data to Trustworthy Human-AI CollaborationIntelligent Computing in Finance BankingIntelligent Control and AutomationReinforcement LearningSustainable Intelligent Computing: Efficient Systems, Algorithms, and Applications
R 4,679.26
Artificial Neural Networks and Machine Learning – ICANN 2026
The five-volume set LNCS 17089-17093 constitutes the proceedings of the 35th International Conference on Artificial Neural Networks and Machine Learning, ICANN 2026, held in Padua, Italy on September 14–17, 2026. The 296 papers included in these conference proceedings were carefully reviewed and selected from 450 submissions. The conference strongly values the synergy between theoretical progress and impactful real-world applications, and actively encourages contributions that demonstrate how artificial neural networks are being used to address pressing societal and technological challenges.
R 3,966.98
Advances in Deep Learning, Artificial Intelligence and Robotics
This book of Advances in Deep Learning, Artificial Intelligence and Robotics (proceedings of ICDLAIR 2020) is intended to be used as a reference by students and researchers who collect scientific and technical contributions with respect to models, tools, technologies and applications in the field of modern artificial intelligence and robotics. Deep Learning, AI and robotics represent key ingredients for the 4th Industrial Revolution. Their extensive application is dramatically changing products and services, with a large impact on labour, economy and society at all. The research and reports of new technologies and applications in DL, AI and robotics like biometric recognition systems, medical diagnosis, industries, telecommunications, AI petri nets model-based diagnosis, gaming, stock trading, intelligent aerospace systems, robot control and web intelligence aim to bridge the gap between these non-coherent disciplines of knowledge and fosters unified development in next-generation computational models for machine intelligence.
R 7,354.59
Machine Learning and Intelligent Communications
This two volume set constitutes the refereed post-conference proceedings of the Second International Conference on Machine Learning and Intelligent Communications, MLICOM 2017, held in Weihai, China, in August 2017. The 143 revised full papers were carefully selected from 225 submissions. The papers are organized thematically in machine learning, intelligent positioning and navigation, intelligent multimedia processing and security, intelligent wireless mobile network and security, cognitive radio and intelligent networking, intelligent internet of things, intelligent satellite communications and networking, intelligent remote sensing, visual computing and three-dimensional modeling, green communication and intelligent networking, intelligent ad-hoc and sensor networks, intelligent resource allocation in wireless and cloud networks, intelligent signal processing in wireless and optical communications, intelligent radar signal processing, intelligent cooperative communications and networking.
R 2,245.46
Errant Intelligence
Artificial intelligence is often framed as a quest to replicate the human brain, promising frictionless cognition and a future of seamless automation. But what if this pervasive narrative obscures a deeper, more ‘errant’ truth?Errant Intelligence challenges the prevailing biological and individualistic interpretations of machine learning, arguing instead for a radical understanding of machine intelligence. The book embraces the deviations, inconsistencies, and ‘errant behaviour’ as fundamental to the discovery of new knowledge, moving beyond the illusion of mere optimisation. Drawing on media theory, cybernetics, and a unique psychoanalytic lens, it explores the ‘technological unconscious’ of machine learning. It traces the historical roots of AI, from early automatons and the Turing machine to natural language processing and contemporary machine learning systems. Challenging the idea of an autonomous, self-generating AI, the book exposes the hidden labour, assumed logics, and inherent biases that animate its operation. It re-evaluates computational thinking, insisting on its inherently social, collective, and symbolic character, and revealing how language and logical paradoxes are not obstacles, but constitutive forces that shape intelligent machines.Errant Intelligence offers a vital new framework for understanding the profound co-evolution of human and machine learning. It’s time to ‘unlearn’ our assumptions and embrace the productive ambiguity and fallibility at the core of machine intelligence.
R 2,125.00
Applied Machine Learning for Healthcare and Life Sciences Using AWS
Build real-world artificial intelligence apps on AWS to overcome challenges faced by healthcare providers and payers, as well as pharmaceutical, life sciences research, and commercial organizationsKey FeaturesLearn about healthcare industry challenges and how machine learning can solve themExplore AWS machine learning services and their applications in healthcare and life sciencesDiscover practical coding instructions to implement machine learning for healthcare and life sciencesBook DescriptionWhile machine learning is not new, it's only now that we are beginning to uncover its true potential in the healthcare and life sciences industry. The availability of real-world datasets and access to better compute resources have helped researchers invent applications that utilize known AI techniques in every segment of this industry, such as providers, payers, drug discovery, and genomics.This book starts by summarizing the introductory concepts of machine learning and AWS machine learning services. You'll then go through chapters dedicated to each segment of the healthcare and life sciences industry. Each of these chapters has three key purposes -- First, to introduce each segment of the industry, its challenges, and the applications of machine learning relevant to that segment. Second, to help you get to grips with the features of the services available in the AWS machine learning stack like Amazon SageMaker and Amazon Comprehend Medical. Third, to enable you to apply your new skills to create an ML-driven solution to solve problems particular to that segment. The concluding chapters outline future industry trends and applications.By the end of this book, you'll be aware of key challenges faced in applying AI to healthcare and life sciences industry and learn how to address those challenges with confidence.What you will learnExplore the healthcare and life sciences industryFind out about the key applications of AI in different industry segmentsApply AI to medical images, clinical notes, and patient dataDiscover security, privacy, fairness, and explainability best practicesExplore the AWS ML stack and key AI services for the industryDevelop practical ML skills using code and AWS servicesDiscover all about industry regulatory requirementsWho this book is forThis book is specifically tailored toward technology decision-makers, data scientists, machine learning engineers, and anyone who works in the data engineering role in healthcare and life sciences organizations. Whether you want to apply machine learning to overcome common challenges in the healthcare and life science industry or are looking to understand the broader industry AI trends and landscape, this book is for you. This book is filled with hands-on examples for you to try as you learn about new AWS AI concepts.
R 1,433.00
