Search results for "Machine Learning for Pattern Recognition"
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Methods and Applications for Modeling and Simulation of Complex Systems
This book constitutes the refereed proceedings of the 23rd Asia Simulation Conference on Methods and Applications for Modeling and Simulation of Complex Systems, AsiaSim 2024, held in Kobe, Japan, during September 17–20, 2024.The 28 full papers presented here, were carefully selected and reviewed from 120 submissions. These papers have been categorized into the following topical sections: Methods for Simulation and Modeling; Simulation for Science, Industry and Society; Interdisciplinary Simulation and Machine Learning; Networks and Complex Systems & Modeling, Simulaiton, and Visualization of Digital Twin.
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Document Analysis and Recognition – ICDAR 2026
The 4-volume set LNCS 16972-16975 constitutes the proceedings of the 20th International Conference on Document Analysis and Recognition, ICDAR 2026, which took place in Vienna, Austria, from August 31 to September 2, 2026. The 140 full papers included in the proceedings were carefully reviewed and selected from 300 submissions. In addition, this proceedings includes 8 competition reports which were also reviewed, separately from the main submission process.The papers were organized into topical sections as follows:Part I: Document Analysis Systems; Document Forensics and Provenance; Document Image Processing; Benchmarks and Datasets; Graphics Recognition; Handwriting Recognition; Historical Document Analysis; Layout Analysis; Recognition of Tables and Formulas; Text and Symbol Recognition;Part II: Poster Papers;Part III: Poster Papers;Part IV: Poster Papers; Competitions.
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Art and Science of Machine Intelligence
This volume presents several machine intelligence technologies, developed over recent decades, and illustrates how they can be combined in application. One application, the detection of dementia from patterns in speech, is used throughout to illustrate these combinations. This application is a classic stationary pattern detection task, so readers may easily see how these combinations can be applied to other similar tasks. The expositions of the methods are supported by the basic theory they rest upon, and their application is clearly illustrated. The book’s goal is to allow readers to select one or more of these methods to quickly apply to their own tasks.Includes a variety of machine intelligent technologies and illustrates how they can work togetherShows evolutionary feature subset selection combined with support vector machines and multiple classifiers combinedIncludes a running case study on intelligent processing relating to Alzheimer’s / dementia detection, in addition to several applications of the machine hybrid algorithms
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Advances in Intelligent Data Analysis XXIII
This volume constitutes the proceedings of the 23rd International Symposium on Intelligent Data Analysis, IDA 2025, which was held in Konstanz, Germany, during May 7–9, 2025.The 35 full papers included in the proceedings were carefully reviewed and selected from 91 submissions. They were organized in topical sections as follows: Applications of data science, foundations of data science; natural language processing; temporal and streaming data; and explainable and interpretable data science.
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Vision-Based Human Activity Recognition
This book offers a systematic, comprehensive, and timely review on V-HAR, and it covers the related tasks, cutting-edge technologies, and applications of V-HAR, especially the deep learning-based approaches. The field of Human Activity Recognition (HAR) has become one of the trendiest research topics due to the availability of various sensors, live streaming of data and the advancement in computer vision, machine learning, etc. HAR can be extensively used in many scenarios, for example, medical diagnosis, video surveillance, public governance, also in human–machine interaction applications. In HAR, various human activities such as walking, running, sitting, sleeping, standing, showering, cooking, driving, abnormal activities, etc., are recognized. The data can be collected from wearable sensors or accelerometer or through video frames or images; among all the sensors, vision-based sensors are now the most widely used sensors due to their low-cost, high-quality, and unintrusive characteristics. Therefore, vision-based human activity recognition (V-HAR) is the most important and commonly used category among all HAR technologies.The addressed topics include hand gestures, head pose, body activity, eye gaze, attention modeling, etc. The latest advancements and the commonly used benchmark are given. Furthermore, this book also discusses the future directions and recommendations for the new researchers.
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Machine Learning and Knowledge Engineering for Decision Making
This book constitutes the refereed proceedings of the 17th Conference on Fuzzy Logic and Intelligent Technologies in Nuclear Science, FLINS 2026, and 21st International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2026, held in Sydney, NSW, Australia, during July 15–19, 2026.The 152 full papers and 8 short papers presented in this book were carefully selected and reviewed from 275 submissions. These papers have been organized in the following topical sections:Part I: Fuzzy Machine Learning and Decision MakingPart II: Knowledge Engineering, LLMs, and ReasoningPart III: Computer Vision and Medical ImagingPart IV: Graph Neural Networks and Relational IntelligencePart V: Trustworthy, Federated, and Robust Learning Systems
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Quantitative Evaluation of Systems and Formal Modeling and Analysis of Timed Systems
This book constitutes the proceedings of the Third International Joint Conference on Quantitative Evaluation of Systems and Formal Modeling and Analysis of Timed Systems, QEST+Formats 2026, which took place in Liverpool, UK, during September 2-4, 2026.The 24 full papers included in this book were carefully reviewed and selected from 54 submissions.They deal with up to date research in the quantitative evaluation and verification of systems, including fundamental and practical aspects of systems with quantitative nature (such as probability, timing, or cost) and particular focus on machine-learning and quantum systems, case studies, and tool papers.
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Deep Learning and Practice with MindSpore
This book systematically introduces readers to the theory of deep learning and explores its practical applications based on the MindSpore AI computing framework. Divided into 14 chapters, the book covers deep learning, deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), unsupervised learning, deep reinforcement learning, automated machine learning, device-cloud collaboration, deep learning visualization, and data preparation for deep learning. To help clarify the complex topics discussed, this book includes numerous examples and links to online resources.
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Multivariate Statistics and Machine Learning
Multivariate Statistics and Machine Learning is a hands-on textbook providing an in-depth guide to multivariate statistics and select machine learning topics using R and Python software.The book offers a theoretical orientation to the concepts required to introduce or review statistical and machine learning topics, and in addition to teaching the techniques, instructs readers on how to perform, implement, and interpret code and analyses in R and Python in multivariate, data science, and machine learning domains. For readers wishing for additional theory, numerous references throughout the textbook are provided where deeper and less “hands on” works can be pursued. With its unique breadth of topics covering a wide range of modern quantitative techniques, user-friendliness, and quality of expository writing, Multivariate Statistics and Machine Learning will serve as a key and unifying introductory textbook for students in the social, natural, statistical, and computational sciences for years to come.
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Statistical Foundations of Actuarial Learning and its Applications
This open access book discusses the statistical modeling of insurance problems, a process which comprises data collection, data analysis and statistical model building to forecast insured events that may happen in the future. It presents the mathematical foundations behind these fundamental statistical concepts and how they can be applied in daily actuarial practice. Statistical modeling has a wide range of applications, and, depending on the application, the theoretical aspects may be weighted differently: here the main focus is on prediction rather than explanation. Starting with a presentation of state-of-the-art actuarial models, such as generalized linear models, the book then dives into modern machine learning tools such as neural networks and text recognition to improve predictive modeling with complex features. Providing practitioners with detailed guidance on how to apply machine learning methods to real-world data sets, and how to interpret the results without losing sight of the mathematical assumptions on which these methods are based, the book can serve as a modern basis for an actuarial education syllabus.
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From Curve Fitting to Machine Learning
This successful book provides in its second edition an interactive and illustrative guide from two-dimensional curve fitting to multidimensional clustering and machine learning with neural networks or support vector machines. Along the way topics like mathematical optimization or evolutionary algorithms are touched. All concepts and ideas are outlined in a clear cut manner with graphically depicted plausibility arguments and a little elementary mathematics.The major topics are extensively outlined with exploratory examples and applications. The primary goal is to be as illustrative as possible without hiding problems and pitfalls but to address them. The character of an illustrative cookbook is complemented with specific sections that address more fundamental questions like the relation between machine learning and human intelligence.All topics are completely demonstrated with the computing platform Mathematica and the Computational Intelligence Packages (CIP), a high-level function library developed with Mathematica's programming language on top of Mathematica's algorithms. CIP is open-source and the detailed code used throughout the book is freely accessible.The target readerships are students of (computer) science and engineering as well as scientific practitioners in industry and academia who deserve an illustrative introduction. Readers with programming skills may easily port or customize the provided code. "'From curve fitting to machine learning' is ... a useful book. ... It contains the basic formulas of curve fitting and related subjects and throws in, what is missing in so many books, the code to reproduce the results.All in all this is an interesting and useful book both for novice as well as expert readers. For the novice it is a good introductory book and the expert will appreciate the many examples and working code". Leslie A. Piegl (Review of the first edition, 2012).
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Trustworthy Machine Learning under Imperfect Data
The subject of this book centresaround trustworthy machine learning under imperfect data. It is primarily designed forscientists, researchers, practitioners, professionals, postgraduates andundergraduates in thefield of machine learning and artificial intelligence. The book focuseson trustworthy deep learning under various types of imperfect data, includingnoisy labels, adversarial examples, and out-of-distribution data. It coverstrustworthy machine learning algorithms, theories, and systems. The main goal of the book is to provide students and researchers in academia with anunbiased and comprehensive literature review. More importantly, it aims to stimulateinsightful discussions about the future of trustworthy machine learning. By engaging the audiencein more in-depth conversations, the book intends to spark ideas for addressing coreproblems in this topic. For example, it will explore how to build up benchmark datasets innoisy-supervised learning, how to tackle the emerging adversarial learning, andhow to tackle out-of-distribution detection. For practitioners in the industry, this book will present state-of-the-art trustworthy machine learning methods tohelp them solve real-world problems in different scenarios, such as onlinerecommendation and web search. While the book will introduce the basics ofknowledge required, readers will benefit from having some familiarity withlinear algebra, probability, machine learning, and artificial intelligence. Theemphasis will be on conveying the intuition behind all formal concepts, theories, and methodologies, ensuring the book remains self-contained at a highlevel.
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Soft Computing for Recognition based on Biometrics
We describe in this book, bio-inspired models and applications of hybrid intel- gent systems using soft computing techniques for image analysis and pattern r- ognition based on biometrics and other information sources. Soft Computing (SC) consists of several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful hybrid intelligent systems. The book is organized in five main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of classification methods and applications, which are basically papers that propose new models for classification to solve general pr- lems and applications. The second part contains papers with the main theme of modular neural networks in pattern recognition, which are basically papers using bio-inspired techniques, like modular neural networks, for achieving pattern r- ognition based on biometric measures. The third part contains papers with the theme of bio-inspired optimization methods and applications to diverse problems. The fourth part contains papers that deal with general theory and algorithms of bio-inspired methods, like neural networks and evolutionary algorithms. The fifth part contains papers on computer vision applications of soft computing methods. In the part of classification methods and applications there are 5 papers that - scribe different contributions on fuzzy logic and bio-inspired models with appli- tion in classification for medical images and other data.
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Information Processing in Medical Imaging
This two-volume set LNCS 15829-15830 constitutes the proceedings of the 29th International Conference on Information Processing in Medical Imaging, IPMI 2025, held on Kos, Greece, during May 25-30, 2025. The 51 full papers presented in this volume were carefully reviewed and selected from 143 submissions. They were organized in topical sections as follows: Part I: Classification/Detection; Registration; Reconstruction; Image synthesis; Image enhancement; and Segmentation.Part II: Computer-aided diagnosis/surgery; Brain; Diffusion models; Self-supervised learning; Vision-language models; Shape analysis; and Time-series image analysis.
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Deep Learning for Hyperspectral Image Analysis and Classification
This book focuses on deep learning-based methods for hyperspectral image (HSI) analysis. Unsupervised spectral-spatial adaptive band-noise factor-based formulation is devised for HSI noise detection and band categorization. The method to characterize the bands along with the noise estimation of HSIs will benefit subsequent remote sensing techniques significantly.This book develops on two fronts: On the one hand, it is aimed at domain professionals who want to have an updated overview of how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields. On the other hand, the authors want to target the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectral data from a multidisciplinary perspective. The presence of these two viewpoints and the inclusion of application fields of remote sensing by deep learning are the original contributions of this review, which also highlights some potentialities and critical issues related to the observed development trends.
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Machine Learning For Malware Detection
Malware detection is exceedingly challenging, requiring deeper expertise in two domains: machine learning (ML) and malware analysis.This comprehensive collection guides ML practitioners and researchers into the state-of-the-art and underlying insights needed to work in and research problems at the intersection of machine learning and cyber security, particularly around the problem of malware targeting the Windows platform.This useful volume's contributions come from a range of experts, representing large companies, major universities, and government labs, all working at the frontiers of malware detection and remediation research.
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Deep Learning for Computational Imaging
Computational techniques for image reconstruction problems enable imaging technologies including high-resolution microscopy, astronomy and seismology, computed tomography, and magnetic resonance imaging. Until recently, methods for solving such inverse problems were derived by experts without any learning. Now, the best performing image reconstruction methods are based on deep learning. This textbook gives the first comprehensive introduction to deep learning based image reconstruction methods. This book first introduces important inverse problems in imaging, including denoising and reconstructing an image from few and noisy measurements, and explains what makes those problems hard and interesting. Then, the book briefly discusses traditional optimization and sparsity based reconstruction methods, as well as optimization techniques as a basis for training and deriving deep neural networks for image reconstruction. The main part of the book is about how to solve image reconstruction problems with deep learning techniques: The book first disuses supervised deep learning approaches that map a measurement to an image as well as network architectures for imaging including convolutional neural networks and transformers. Then, reconstruction approaches based on generative models such as variational autoencoders and diffusion models are discussed, and how un-trained neural networks and implicit neural representations enable signal and image reconstruction. The book ends with a discussion on the robustness of deep learning based reconstruction as well as a discussion on the important topic of evaluating models and datasets, which are a critical ingredient of deep learning based imaging.
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Computational Learning Theory
Computational learning theory is a subject which has been advancing rapidly in the last few years. The authors concentrate on the probably approximately correct model of learning, and gradually develop the ideas of efficiency considerations. Finally, applications of the theory to artificial neural networks are considered. Many exercises are included throughout, and the list of references is extensive. This volume is relatively self contained as the necessary background material from logic, probability and complexity theory is included. It will therefore form an introduction to the theory of computational learning, suitable for a broad spectrum of graduate students from theoretical computer science and mathematics.
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Optimization, Machine Learning, and Fuzzy Logic: Theory, Algorithms, and Applications
Optimization, machine learning, and fuzzy logic are fundamental in the field of computational intelligence, each contributing to solving complex problems across various domains. Optimization techniques focus on finding the best solutions to problems by improving efficiency and minimizing resources. Machine learning enables systems to learn from data, making predictions or decisions without being programmed. Fuzzy logic deals with uncertainty and imprecision, allowing for flexible decision-making processes. Together, these theories, algorithms, and applications solve challenges in fields such as engineering, finance, and healthcare, where traditional methods often fall short. The continued application and exploration of these disciplines may unveil new possibilities for advanced problem-solving and intelligent systems. Optimization, Machine Learning, and Fuzzy Logic: Theory, Algorithms, and Applications explores optimization techniques, fuzzy logic, and their integration with machine learning. It covers fundamental concepts, mathematical foundations, algorithms, and applications, providing a holistic understanding of these domains. This book covers topics such as disease detection, deep learning, and text analysis, and is a useful resource for engineers, data scientists, medical professionals, academicians, and researchers.
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Machine Learning Meets Medical Imaging
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Learning in Non-Stationary Environments
Recent decades have seen rapid advances in automatization processes, supported by modern machines and computers. The result is significant increases in system complexity and state changes, information sources, the need for faster data handling and the integration of environmental influences. Intelligent systems, equipped with a taxonomy of data-driven system identification and machine learning algorithms, can handle these problems partially. Conventional learning algorithms in a batch off-line setting fail whenever dynamic changes of the process appear due to non-stationary environments and external influences. Learning in Non-Stationary Environments: Methods and Applications offers a wide-ranging, comprehensive review of recent developments and important methodologies in the field. The coverage focuses on dynamic learning in unsupervised problems, dynamic learning in supervised classification and dynamic learning in supervised regression problems. A later section is dedicated to applications in which dynamic learning methods serve as keystones for achieving models with high accuracy. Rather than rely on a mathematical theorem/proof style, the editors highlight numerous figures, tables, examples and applications, together with their explanations. This approach offers a useful basis for further investigation and fresh ideas and motivates and inspires newcomers to explore this promising and still emerging field of research.
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Machine Learning for Intelligent Decision Science
The book discusses machine learning-based decision-making models, and presents intelligent, hybrid and adaptive methods and tools for solving complex learning and decision-making problems under conditions of uncertainty. Featuring contributions from data scientists, practitioners and educators, the book covers a range of topics relating to intelligent systems for decision science, and examines recent innovations, trends, and practical challenges in the field. The book is a valuable resource for academics, students, researchers and professionals wanting to gain insights into decision-making.
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Machine Learning for Cybersecurity
This SpringerBrief presents the underlying principles of machine learning and how to deploy various deep learning tools and techniques to tackle and solve certain challenges facing the cybersecurity industry.By implementing innovative deep learning solutions, cybersecurity researchers, students and practitioners can analyze patterns and learn how to prevent cyber-attacks and respond to changing malware behavior. The knowledge and tools introduced in this brief can also assist cybersecurity teams to become more proactive in preventing threats and responding to active attacks in real time. It can reduce the amount of time spent on routine tasks and enable organizations to use their resources more strategically. In short, the knowledge and techniques provided in this brief can help make cybersecurity simpler, more proactive, less expensive and far more effectiveAdvanced-level students in computer science studying machine learning with a cybersecurity focus will find this SpringerBrief useful as a study guide. Researchers and cybersecurity professionals focusing on the application of machine learning tools and techniques to the cybersecurity domain will also want to purchase this SpringerBrief.
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Machine Learning and Soft Computing
This two part-volume CCIS constitutes the refereed proceedings of 9th International Conference, ICMLSC 2025, in Tokyo, Japan in January 24–26, 2025. The 39 full papers and 13 short papers included in this book were carefully reviewed and selected from 121 submissions. They follow the topical sections as below:Part I : Multimodal Data Analysis and Model Optimization; Basic Theories of Machine Learning and Emerging Application Technologies; and Intelligent Recommendation System Design and Privacy Security.Part II : Deep Learning Models and High-performance Computing; Data-driven Complex System Modeling and Intelligent Optimization Algorithms; and Image Analysis and Processing Methods based on AI.
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Deep Learning Applications in Image Analysis
This book provides state-of-the-art coverage of deep learning applications in image analysis. The book demonstrates various deep learning algorithms that can offer practical solutions for various image-related problems; also how these algorithms are used by scientists and scholars in industry and academia. This includes autoencoder and deep convolutional generative adversarial network in improving classification performance of Bangla handwritten characters, dealing with deep learning-based approaches using feature selection methods for automatic diagnosis of covid-19 disease from x-ray images, imbalance image data sets of classification, image captioning using deep transfer learning, developing a vehicle over speed detection system, creating an intelligent system for video-based proximity analysis, building a melanoma cancer detection system using deep learning, plant diseases classification using AlexNet, dealing with hyperspectral images using deep learning, chest x-ray image classification of pneumonia disease using efficient net and inceptionv3.The book also addresses the difficulty of implementing deep learning in terms of computation time and the complexity of reasoning and modelling different types of data where information is currently encoded. Each chapter has the application of various new or existing deep learning models such as Deep Neural Network (DNN) and Deep Convolutional Neural Networks (DCNN). The detailed utilization of deep learning packages that are available in MATLAB, Python and R programming environments have also been discussed, therefore, the readers will get to know about the practical implementation of deep learning as well. The content of this book is presented in a simple and lucid style for professionals, nonprofessionals, scientists, and students interested in the research area of deep learning applications in image analysis.
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Neural Information Processing
The sixteen-volume set, CCIS 2282-2297, constitutes the refereed proceedings of the 31st International Conference on Neural Information Processing, ICONIP 2024, held in Auckland, New Zealand, in December 2024.The 472 regular papers presented in this proceedings set were carefully reviewed and selected from 1301 submissions. These papers primarily focus on the following areas: Theory and algorithms; Cognitive neurosciences; Human-centered computing; and Applications.
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Neural Information Processing
The sixteen-volume set, CCIS 2282-2297, constitutes the refereed proceedings of the 31st International Conference on Neural Information Processing, ICONIP 2024, held in Auckland, New Zealand, in December 2024. The 472 regular papers presented in this proceedings set were carefully reviewed and selected from 1301 submissions. These papers primarily focus on the following areas: Theory and algorithms; Cognitive neurosciences; Human-centered computing; and Applications.
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Machine Learning and Principles and Practice of Knowledge Discovery in Databases
The 4-volume set CCIS 2558 – 2561 constitutes the refereed proceedings of several workshops held in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2024, which took place in Vilnius, Lithuania, in September 2024. The 132 full papers included in these proceedings were carefully reviewed and selected from a total of 272 submissions to the workshops. The papers were organized topical sections as follows:Part I: Workshop on IoT, Edge, and Mobile for Embedded Machine Learning (ITEM Workshop 2024), Machine Learning for Sustainable Power Systems (ML4SPS), Workshop on eXplainable Knowledge Discovery in Data Mining (XKDD) and Workshop on Bias and Fairness in AI (BIAS 2024).Part II: Workshop on Machine Learning for Chemistry and Chemical Engineering (ML4CCE), Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence (AIM[1]LAI), 2nd Workshop on Hybrid Human-Machine Learning and Decision Making HLDM’24, 15th International Workshop on Machine Learning and Music (MML 2024), Tutorial and Workshop on Responsible Knowledge Discovery in Education (RKDE 2024).Part III: 21st International Workshop on Mining and Learning with Graphs (MLG 2024), 2nd International Workshop on Deep Learning meets Neuromorphic Hard[1]ware (website), International Workshop on Machine Learning for Irregular Time Series (ML4ITS 2024), Workshop on Machine Learning for Earth Observation (MACLEAN 2024) Workshop on New Frontiers in Mining Complex Patterns (NFMCP 2024) and International Workshop and Tutorial on Data-Centric Artificial Intelligence (DEARING).Part IV: Workshop on Data Science for Social Good (SoGood 2024), 7th Workshop on Machine Learning for CyberSecurity (MLCS 2024), Tutorial and Workshop on Explainable and Robust AI for Industry 4.0 & 5.0 (X-RAI), Workshop on MIning DAta for financial applicationS (MIDAS), Workshop on Advancements in Federated Learning (WAFL), Workshop on Mining and Learning Real-world Dynamics via High-order Networks (MLH).
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Zeichenerkennung durch biologische und technische Systeme / Pattern Recognition in Biological and Technical Systems
Auf den vorausgegangenen Kongressen der Deutschen Gesellschaft fur Kybernetik wurden Vortrage aus dem Gesamtgebiet der biologischen, psychologischen und technischen Kybernetik gehalten. Fitr den 4. KongreB hat das Prasidium erstmals beschlossen, die Themen der Vortrage auf ein Teilgebiet der Kybernetik zu kon- zentrieren. DasProgrammkomitee (O. -J. Grosser, H. Marko, H. Mittelstaedt) wahlte Vortrage uber das Problem der Zeichenerkennung aus, da vermutet werden konnte, daB auf diesem Gebiet ein Dialog zwischen technischer, psychologischer und bio- logischer Forschung besonders sinnvoll ist. Ferner wurde in das Programm ein Vortrag uber die Vorgeschichte der Kybernetik aufgenommen und in der ge- druckten Fassung an die Spitze des Bandes gestellt. Dieser Beitrag schien uns insbesondere wegen nicht allgemein bekannter Tatsachen uber die wissenschaft lichen Leistungen von Felix Lincke (1840-1917) und Herrmann Schmidt (1894-1968) interessant zu sein. Wir hoffen, daB durch die Herausgabe des KongreBbandes dem Leser ein umfangreicher "Oberblick uber die verschiedenen Forschungsaspekte auf dem Gebiet der Zeichenerkennung gegeben wird.Der Kongress wurde durch Beihilfen des Senators fiir Wirtschaft und des Senators fur Wissenschaft und Kunst, Berlin, gefOrdert, wofiir auch an dieser Stelle gedankt sei. The papers read at the previous meetings of the Deutsche Gesellschaft fiir Kyber- netik encompassed the entire field of biological, psychological and technical cybernetics. For the Fourth Congress, it was decided to concentrate for the first time on one area of cybernetics. The Program Committee (O. -J. Grusser, H. Marko, H.
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Practical Deep Learning at Scale with MLflow
Train, test, run, track, store, tune, deploy, and explain provenance-aware deep learning models and pipelines at scale with reproducibility using MLflowKey FeaturesFocus on deep learning models and MLflow to develop practical business AI solutions at scaleShip deep learning pipelines from experimentation to production with provenance trackingLearn to train, run, tune and deploy deep learning pipelines with explainability and reproducibilityBook DescriptionThe book starts with an overview of the deep learning (DL) life cycle and the emerging Machine Learning Ops (MLOps) field, providing a clear picture of the four pillars of deep learning: data, model, code, and explainability and the role of MLflow in these areas. From there onward, it guides you step by step in understanding the concept of MLflow experiments and usage patterns, using MLflow as a unified framework to track DL data, code and pipelines, models, parameters, and metrics at scale. You'll also tackle running DL pipelines in a distributed execution environment with reproducibility and provenance tracking, and tuning DL models through hyperparameter optimization (HPO) with Ray Tune, Optuna, and HyperBand. As you progress, you'll learn how to build a multi-step DL inference pipeline with preprocessing and postprocessing steps, deploy a DL inference pipeline for production using Ray Serve and AWS SageMaker, and finally create a DL explanation as a service (EaaS) using the popular Shapley Additive Explanations (SHAP) toolbox. By the end of this book, you'll have built the foundation and gained the hands-on experience you need to develop a DL pipeline solution from initial offline experimentation to final deployment and production, all within a reproducible and open source framework.What you will learnUnderstand MLOps and deep learning life cycle developmentTrack deep learning models, code, data, parameters, and metricsBuild, deploy, and run deep learning model pipelines anywhereRun hyperparameter optimization at scale to tune deep learning modelsBuild production-grade multi-step deep learning inference pipelinesImplement scalable deep learning explainability as a serviceDeploy deep learning batch and streaming inference servicesShip practical NLP solutions from experimentation to productionWho this book is forThis book is for machine learning practitioners including data scientists, data engineers, ML engineers, and scientists who want to build scalable full life cycle deep learning pipelines with reproducibility and provenance tracking using MLflow. A basic understanding of data science and machine learning is necessary to grasp the concepts presented in this book.
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Rule-Based Evolutionary Online Learning Systems
Rule-basedevolutionaryonlinelearningsystems,oftenreferredtoasMichig- style learning classi?er systems (LCSs), were proposed nearly thirty years ago (Holland, 1976; Holland, 1977) originally calling them cognitive systems. LCSs combine the strength of reinforcement learning with the generali- tion capabilities of genetic algorithms promising a ?exible, online general- ing, solely reinforcement dependent learning system. However, despite several initial successful applications of LCSs and their interesting relations with a- mal learning and cognition, understanding of the systems remained somewhat obscured. Questions concerning learning complexity or convergence remained unanswered. Performance in di?erent problem types, problem structures, c- ceptspaces,andhypothesisspacesstayednearlyunpredictable. Thisbookhas the following three major objectives: (1) to establish a facetwise theory - proachforLCSsthatpromotessystemanalysis,understanding,anddesign;(2) to analyze, evaluate, and enhance the XCS classi?er system (Wilson, 1995) by the means of the facetwise approach establishing a fundamental XCS learning theory; (3) to identify both the major advantages of an LCS-based learning approach as well as the most promising potential application areas. Achieving these three objectives leads to a rigorous understanding of LCS functioning that enables the successful application of LCSs to diverse problem types and problem domains. The quantitative analysis of XCS shows that the inter- tive, evolutionary-based online learning mechanism works machine learning competitively yielding a low-order polynomial learning complexity. Moreover, the facetwise analysis approach facilitates the successful design of more - vanced LCSs including Holland’s originally envisioned cognitive systems. Martin V.
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Machine Learning and Knowledge Discovery in Databases
The multi-volume set LNAI 13713 until 13718 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2022, which took place in Grenoble, France, in September 2022.The 236 full papers presented in these proceedings were carefully reviewed and selected from a total of 1060 submissions. In addition, the proceedings include 17 Demo Track contributions.The volumes are organized in topical sections as follows:Part I: Clustering and dimensionality reduction; anomaly detection; interpretability and explainability; ranking and recommender systems; transfer and multitask learning; Part II: Networks and graphs; knowledge graphs; social network analysis; graph neural networks; natural language processing and text mining; conversational systems; Part III: Deep learning; robust and adversarial machine learning; generative models; computer vision; meta-learning, neural architecture search; Part IV: Reinforcement learning; multi-agent reinforcement learning; bandits and online learning; active and semi-supervised learning; private and federated learning; .Part V: Supervised learning; probabilistic inference; optimal transport; optimization; quantum, hardware; sustainability; Part VI: Time series; financial machine learning; applications; applications: transportation; demo track.
R 2,851.09
Preference Learning
The topic of preferences is a new branch of machine learning and data mining, and it has attracted considerable attention in artificial intelligence research in previous years. It involves learning from observations that reveal information about the preferences of an individual or a class of individuals. Representing and processing knowledge in terms of preferences is appealing as it allows one to specify desires in a declarative way, to combine qualitative and quantitative modes of reasoning, and to deal with inconsistencies and exceptions in a flexible manner. And, generalizing beyond training data, models thus learned may be used for preference prediction. This is the first book dedicated to this topic, and the treatment is comprehensive. The editors first offer a thorough introduction, including a systematic categorization according to learning task and learning technique, along with a unified notation. The first half of the book is organized into parts on label ranking, instance ranking, and object ranking; while the second half is organized into parts on applications of preference learning in multiattribute domains, information retrieval, and recommender systems. The book will be of interest to researchers and practitioners in artificial intelligence, in particular machine learning and data mining, and in fields such as multicriteria decision-making and operations research.
R 6,508.47
Machine Learning and Knowledge Engineering for Decision Making
This book constitutes the refereed proceedings of the 17th Conference on Fuzzy Logic and Intelligent Technologies in Nuclear Science, FLINS 2026, and 21st International Conference on Intelligent Systems and Knowledge Engineering, ISKE 2026, held in Sydney, NSW, Australia, during July 15–19, 2026.The 152 full papers and 8 short papers presented in this book were carefully selected and reviewed from 275 submissions. These papers have been organized in the following topical sections:Part I: Fuzzy Machine Learning and Decision MakingPart II: Knowledge Engineering, LLMs, and ReasoningPart III: Computer Vision and Medical ImagingPart IV: Graph Neural Networks and Relational IntelligencePart V: Trustworthy, Federated, and Robust Learning Systems
R 2,204.30
Machine Learning and Principles and Practice of Knowledge Discovery in Databases
The 4-volume set CCIS 2558 – 2561 constitutes the refereed proceedings of several workshops held in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2024, which took place in Vilnius, Lithuania, in September 2024. The 132 full papers included in these proceedings were carefully reviewed and selected from a total of 272 submissions to the workshops. The papers were organized topical sections as follows:Part I: Workshop on IoT, Edge, and Mobile for Embedded Machine Learning (ITEM Workshop 2024), Machine Learning for Sustainable Power Systems (ML4SPS), Workshop on eXplainable Knowledge Discovery in Data Mining (XKDD) and Workshop on Bias and Fairness in AI (BIAS 2024).Part II: Workshop on Machine Learning for Chemistry and Chemical Engineering (ML4CCE), Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence (AIM[1]LAI), 2nd Workshop on Hybrid Human-Machine Learning and Decision Making HLDM’24, 15th International Workshop on Machine Learning and Music (MML 2024), Tutorial and Workshop on Responsible Knowledge Discovery in Education (RKDE 2024).Part III: 21st International Workshop on Mining and Learning with Graphs (MLG 2024), 2nd International Workshop on Deep Learning meets Neuromorphic Hard[1]ware (website), International Workshop on Machine Learning for Irregular Time Series (ML4ITS 2024), Workshop on Machine Learning for Earth Observation (MACLEAN 2024) Workshop on New Frontiers in Mining Complex Patterns (NFMCP 2024) and International Workshop and Tutorial on Data-Centric Artificial Intelligence (DEARING).Part IV: Workshop on Data Science for Social Good (SoGood 2024), 7th Workshop on Machine Learning for CyberSecurity (MLCS 2024), Tutorial and Workshop on Explainable and Robust AI for Industry 4.0 & 5.0 (X-RAI), Workshop on MIning DAta for financial applicationS (MIDAS), Workshop on Advancements in Federated Learning (WAFL), Workshop on Mining and Learning Real-world Dynamics via High-order Networks (MLH).
R 6,604.41
R 1,596.00
Advanced Machine Learning Technologies and Applications
This book presents the refereed proceedings of the 5th International Conference on Advanced Machine Learning Technologies and Applications (AMLTA 2020), held at Manipal University Jaipur, India, on February 13 – 15, 2020, and organized in collaboration with the Scientific Research Group in Egypt (SRGE). The papers cover current research in machine learning, big data, Internet of Things, biomedical engineering, fuzzy logic and security, as well as intelligence swarms and optimization.
R 11,581.33
