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Process Mining Techniques for Pattern Recognition
This book focuses on the theory, practice, and concepts of process mining techniques in detail, especially pattern recognition in diverse society, science, medicine, engineering, and business. The book deliberates several perspectives on process mining techniques in the broader context of data science and big data approaches. Process Mining Techniques for Pattern Recognition: Concepts, Theory, and Practice provides an introduction to process mining techniques and pattern recognition. After that, it delivers the fundamentals of process modelling and mining essential to comprehend the book. The text emphasizes discovery as an important process mining task and includes case studies as well as real-life examples to guide users in successfully applying process mining techniques for pattern recognition in practice.Intended to be an introduction to process mining and pattern recognition for students, academics, and practitioners, this book is perfect for those who want to learn the basics, and also gain an understanding of the concepts on a deeper level.
R 9,300.13
Process Mining Techniques for Pattern Recognition
This book focuses on the theory, practice, and concepts of process mining techniques in detail, especially pattern recognition in diverse society, science, medicine, engineering, and business. The book deliberates several perspectives on process mining techniques in the broader context of data science and big data approaches. Process Mining Techniques for Pattern Recognition: Concepts, Theory, and Practice provides an introduction to process mining techniques and pattern recognition. After that, it delivers the fundamentals of process modelling and mining essential to comprehend the book. The text emphasizes discovery as an important process mining task and includes case studies as well as real-life examples to guide users in successfully applying process mining techniques for pattern recognition in practice.Intended to be an introduction to process mining and pattern recognition for students, academics, and practitioners, this book is perfect for those who want to learn the basics, and also gain an understanding of the concepts on a deeper level.
R 2,781.60
Process Mining
This is the second edition of Wil van der Aalst’s seminal book on process mining, which now discusses the field also in the broader context of data science and big data approaches. It includes several additions and updates, e.g. on inductive mining techniques, the notion of alignments, a considerably expanded section on software tools and a completely new chapter of process mining in the large. It is self-contained, while at the same time covering the entire process-mining spectrum from process discovery to predictive analytics. After a general introduction to data science and process mining in Part I, Part II provides the basics of business process modeling and data mining necessary to understand the remainder of the book. Next, Part III focuses on process discovery as the most important process mining task, while Part IV moves beyond discovering the control flow of processes, highlighting conformance checking, and organizational and time perspectives. Part V offers a guide to successfully applying process mining in practice, including an introduction to the widely used open-source tool ProM and several commercial products. Lastly, Part VI takes a step back, reflecting on the material presented and the key open challenges. Overall, this book provides a comprehensive overview of the state of the art in process mining. It is intended for business process analysts, business consultants, process managers, graduate students, and BPM researchers.
R 3,114.00
Pattern Recognition Algorithms for Data Mining
Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results. Tasks covered include data condensation, feature selection, case generation, clustering/classification, and rule generation and evaluation. This volume presents various theories, methodologies, and algorithms, using both classical approaches and hybrid paradigms. The authors emphasize large datasets with overlapping, intractable, or nonlinear boundary classes, and datasets that demonstrate granular computing in soft frameworks.Organized into eight chapters, the book begins with an introduction to PR, data mining, and knowledge discovery concepts. The authors analyze the tasks of multi-scale data condensation and dimensionality reduction, then explore the problem of learning with support vector machine (SVM). They conclude by highlighting the significance of granular computing for different mining tasks in a soft paradigm.
R 9,315.40
Pattern Recognition and Data Mining
This LNCS volume contains the papers presented at the 3rd International C- ferenceonAdvances in PatternRecognition(ICAPR2005)organizedin August, 2005 in the beautiful city of Bath, UK. The conference was ?rst organized in November 1998 in Plymouth, UK and subsequently in March 2001 in Rio de Janeiro, Brazil. The conference encouraged papers that made signi?cant th- reticalandapplication-basedcontributions inpatternrecognition. The emphasis was on an open exchange of ideas and shared learning. The papers submitted to ICAPR 2005 were thoroughly reviewed by up to three referees per paper and less than 40% of the submitted papers were accepted. The papers have been ?nally published as two volumes of LNCS and these are organized under the themes of Pattern Recognition and Data Mining (which included papers from the trackson PatternRecognition Methods, Knowledgeand Learning,and Data Mining), and Pattern Recognition and Image Analysis (which included papers from the Applications track). From the conference technical programme point of view, the ?rst volume contains papers on pattern recognition, data mining, signal processing and OCR/document analysis.The second volume contains - pers from the Workshop on Pattern Recognition for Crime Prevention, Security and Surveillance, Biometrics, Image Processing and Medical Imaging. ICAPR 2005was run in parallel with the InternationalWorkshop on Pattern Recognition for Crime Prevention, Security and Surveillance that was organized onthe22ndofAugust,2005. Thisworkshopbroughttogethera number ofexc- lent papers that focussed on how pattern recognition techniques can be used to developsystemsthathelpwithcrimepreventionanddetection. Onthesameday, a number of tutorials were also organized.
R 3,106.55
Data Mining and Knowledge Discovery for Process Monitoring and Control
Modern computer-based control systems are able to collect a large amount of information, display it to operators and store it in databases but the interpretation of the data and the subsequent decision making relies mainly on operators with little computer support. This book introduces developments in automatic analysis and interpretation of process-operational data both in real-time and over the operational history, and describes new concepts and methodologies for developing intelligent, state space-based systems for process monitoring, control and diagnosis. The book brings together new methods and algorithms from process monitoring and control, data mining and knowledge discovery, artificial intelligence, pattern recognition, and causal relationship discovery, as well as signal processing. It also provides a framework for integrating plant operators and supervisors into the design of process monitoring and control systems.
R 3,033.04
Applications and Developments in Semantic Process Mining
As technology becomes increasingly intelligent, various factors within the field of data science are seeing significant transformation. Process analysis is one area that is undergoing substantial development due to the implementation of semantic reasoning and web technologies. The congruence of these two systems has created various applications and developments in data processing and analysis across several professional fields. Applications and Developments in Semantic Process Mining is an essential reference source that discusses the improvement of process mining algorithms through the implementation of semantic modeling and representation. Featuring research on topics such as domain ontologies, fuzzy modeling, and information extraction, the book takes into account the different stages of process mining and its application in real time and then expounds the classical process mining techniques to semantical preparation of the extracted models for further analysis and querying at a more abstract level. The book provides a wide-ranging idea of the application and development of semantic process mining that is expected to be beneficial and used by professionals, software and data engineers, software developers, IT experts, business owners and entrepreneurs, and process analysts.
R 6,389.10
Rough-Fuzzy Pattern Recognition
Learn how to apply rough-fuzzy computing techniques to solve problems in bioinformatics and medical image processing Emphasizing applications in bioinformatics and medical image processing, this text offers a clear framework that enables readers to take advantage of the latest rough-fuzzy computing techniques to build working pattern recognition models. The authors explain step by step how to integrate rough sets with fuzzy sets in order to best manage the uncertainties in mining large data sets. Chapters are logically organized according to the major phases of pattern recognition systems development, making it easier to master such tasks as classification, clustering, and feature selection. Rough-Fuzzy Pattern Recognition examines the important underlying theory as well as algorithms and applications, helping readers see the connections between theory and practice. The first chapter provides an introduction to pattern recognition and data mining, including the key challenges of working with high-dimensional, real-life data sets. Next, the authors explore such topics and issues as: Soft computing in pattern recognition and data miningA mathematical framework for generalized rough sets, incorporating the concept of fuzziness in defining the granules as well as the setSelection of non-redundant and relevant features of real-valued data setsSelection of the minimum set of basis strings with maximum information for amino acid sequence analysisSegmentation of brain MR images for visualization of human tissues Numerous examples and case studies help readers better understand how pattern recognition models are developed and used in practice. This textcovering the latest findings as well as directions for future researchis recommended for both students and practitioners working in systems design, pattern recognition, image analysis, data mining, bioinformatics, soft computing, and computational intelligence.
R 4,232.00
Machine Learning and Data Mining in Pattern Recognition
The field of machine learning and data mining in connection with pattern recognition enjoys growing popularity and attracts many researchers. Automatic pattern recognition systems have proven successful in many applications. The wide use of these systems depends on their ability to adapt to changing environmental conditions and to deal with new objects. This requires learning capabilities on the parts of these systems. The exceptional attraction of learning in pattern recognition lies in the specific data themselves and the different stages at which they get processed in a pattern recognition system. This results a specific branch within the field of machine learning. At the workshop, were presented machine learning approaches for image pre-processing, image segmentation, recognition and interpretation. Machine learning systems were shown on applications such as document analysis and medical image analysis. Many databases are developed that contain multimedia sources such as images, measurement protocols, and text documents. Such systems should be able to retrieve these sources by content. That requires specific retrieval and indexing strategies for images and signals. Higher quality database contents can be achieved if it were possible to mine these databases for their underlying information. Such mining techniques have to consider the specific characteristic of the image sources. The field of mining multimedia databases is just starting out. We hope that our workshop can attract many other researchers to this subject.
R 1,545.07
Process Mining
This is the second edition of Wil van der Aalst’s seminal book on process mining, which now discusses the field also in the broader context of data science and big data approaches. It includes several additions and updates, e.g. on inductive mining techniques, the notion of alignments, a considerably expanded section on software tools and a completely new chapter of process mining in the large. It is self-contained, while at the same time covering the entire process-mining spectrum from process discovery to predictive analytics. After a general introduction to data science and process mining in Part I, Part II provides the basics of business process modeling and data mining necessary to understand the remainder of the book. Next, Part III focuses on process discovery as the most important process mining task, while Part IV moves beyond discovering the control flow of processes, highlighting conformance checking, and organizational and time perspectives. Part V offers a guide to successfully applying process mining in practice, including an introduction to the widely used open-source tool ProM and several commercial products. Lastly, Part VI takes a step back, reflecting on the material presented and the key open challenges. Overall, this book provides a comprehensive overview of the state of the art in process mining. It is intended for business process analysts, business consultants, process managers, graduate students, and BPM researchers.
R 2,015.00
Image Processing and Pattern Recognition
A comprehensive guide to the essential principles of image processing and pattern recognition Techniques and applications in the areas of image processing and pattern recognition are growing at an unprecedented rate. Containing the latest state-of-the-art developments in the field, Image Processing and Pattern Recognition presents clear explanations of the fundamentals as well as the most recent applications. It explains the essential principles so readers will not only be able to easily implement the algorithms and techniques, but also lead themselves to discover new problems and applications. Unlike other books on the subject, this volume presents numerous fundamental and advanced image processing algorithms and pattern recognition techniques to illustrate the framework. Scores of graphs and examples, technical assistance, and practical tools illustrate the basic principles and help simplify the problems, allowing students as well as professionals to easily grasp even complicated theories. It also features unique coverage of the most interesting developments and updated techniques, such as image watermarking, digital steganography, document processing and classification, solar image processing and event classification, 3-D Euclidean distance transformation, shortest path planning, soft morphology, recursive morphology, regulated morphology, and sweep morphology. Additional topics include enhancement and segmentation techniques, active learning, feature extraction, neural networks, and fuzzy logic. Featuring supplemental materials for instructors and students, Image Processing and Pattern Recognition is designed for undergraduate seniors and graduate students, engineering and scientific researchers, and professionals who work in signal processing, image processing, pattern recognition, information security, document processing, multimedia systems, and solar physics.
R 5,860.00
Process Mining Techniques for Managing and Improving Healthcare Systems
This book discusses a new process mining method along with a detailed comparison between different techniques that provide a complete vision of the process of data acquisition, data analysis, and data prediction.Process Mining Techniques for Managing and Improving Healthcare Systems offers a new framework for process learning which is probabilistic and enables the process to be learned in an accumulative manner. The steps of prediction modeling and building the required knowledge are highlighted throughout the book, along with a strong emphasis on the correlation between the healthcare domain and technology including the different aspects, such as: managing records, information, and procedures; early detection of diseases; and the improvement of accuracy in choosing the right treatment procedures.This reference provides a wealth of knowledge for practitioners, researchers, and students at the basic and intermediary levels working within the healthcare system, computer science, electronics and communications, as well as medical providers and also hospital management entities.
R 2,787.18
Pattern Recognition and Machine Intelligence
This book constitutes the proceedings of the 6th International Conference on Pattern Recognition and Machine Intelligence, PReMI 2015, held in Warsaw, Poland, in June/July 2015. The total of 53 full papers and 1 short paper presented in this volume were carefully reviewed and selected from 90 submissions. They were organized in topical sections named: foundations of machine learning; image processing; image retrieval; image tracking; pattern recognition; data mining techniques for large scale data; fuzzy computing; rough sets; bioinformatics; and applications of artificial intelligence.
R 1,607.80
Pattern Recognition
This book constitutes the proceedings of the 10th Mexican Conference on Pattern Recognition, MCPR 2018, held in Puebla, Mexico, in June 2018. The 28 papers presented in this volume were carefully reviewed and selected from 44 submissions. They were organized in topical sections named: pattern recognition principles; deep learning, neural networks and associative memories; data mining; and computer vision.
R 2,364.56
Machine Learning and Data Mining in Pattern Recognition
There is no royal road to science, and only those who do not dread the fatiguing climb of its steep paths have a chance of gaining its luminous summits. Karl Marx A Universial Genius of the 19th Century Many scientists from all over the world during the past two years since the MLDM 2007 have come along on the stony way to the sunny summit of science and have worked hard on new ideas and applications in the area of data mining in pattern r- ognition. Our thanks go to all those who took part in this year's MLDM. We appre- ate their submissions and the ideas shared with the Program Committee. We received over 205 submissions from all over the world to the International Conference on - chine Learning and Data Mining, MLDM 2009. The Program Committee carefully selected the best papers for this year’s program and gave detailed comments on each submitted paper. There were 63 papers selected for oral presentation and 17 papers for poster presentation. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data-mining methods for the different multimedia data types such as image mining, text mining, video mining and Web mining. Among these topics this year were special contributions to subtopics such as attribute discre- zation and data preparation, novelty and outlier detection, and distances and simila- ties.
R 3,125.19
Pattern Recognition
This 2-volume set LNCS 15297-15298 constitutes the refereed proceedings of the 46th Annual Conference of the German Association for Pattern Recognition, DAGM-GCPR 2024, held in Munich, Germany, during September 10-13, 2024.The 44 full papers included in these proceedings were carefully reviewed and selected from 81 submissions. They are organized in these topical sections:Part I: Clustering and Segmentation; Learning Techniques; Medical and Biological Applications; Uncertainty and Explainability.Part II: Modelling of Faces and Shapes; Image Generation and Reconstruction; 3D Analysis and Sythesis; Video Analysis; Photogrammetry and Remote Sensing.
R 2,951.43
Pattern Recognition
This book constitutes the proceedings of the 12th Mexican Conference on Pattern Recognition, MCPR 2020, which was due to be held in Morelia, Mexico, in June 2020. The conference was held virtually due to the COVID-19 pandemic.The 31 papers presented in this volume were carefully reviewed and selected from 67 submissions. They were organized in the following topical sections: pattern recognition techniques; image processing and analysis; computer vision; industrial and medical applications of pattern recognition; natural language processing and recognition; artificial intelligence techniques and recognition.
R 2,183.15
Pattern Recognition
Explores the heart of pattern recognition concepts, methods and applications using statistical, syntactic and neural approaches. Divided into four sections, it clearly demonstrates the similarities and differences among the three approaches. The second part deals with the statistical pattern recognition approach, starting with a simple example and finishing with unsupervised learning through clustering. Section three discusses the syntactic approach and explores such topics as the capabilities of string grammars and parsing; higher dimensional representations and graphical approaches. Part four presents an excellent overview of the emerging neural approach including an examination of pattern associations and feedforward nets. Along with examples, each chapter provides the reader with pertinent literature for a more in-depth study of specific topics.
R 8,326.00
Machine Interpretation Of Patterns: Image Analysis And Data Mining
This review volume provides from both theoretical and application points of views, recent developments and state-of-the-art reviews in various areas of pattern recognition, image processing, machine learning, soft computing, data mining and web intelligence.Machine Interpretation of Patterns: Image Analysis and Data Mining is an essential and invaluable resource for professionals and advanced graduates in computer science, mathematics and life sciences. It can also be considered as an integrated volume to researchers interested in doing interdisciplinary research where computer science is a component.
R 4,466.11
Proceedings of International Conference on Cognition and Recognition
The book covers a comprehensive overview of the theory, methods, applications and tools of cognition and recognition. The book is a collection of best selected papers presented in the International Conference on Cognition and Recognition 2016 (ICCR 2016) and helpful for scientists and researchers in the field of image processing, pattern recognition and computer vision for advance studies. Nowadays, researchers are working in interdisciplinary areas and the proceedings of ICCR 2016 plays a major role to accumulate those significant works at one place. The chapters included in the proceedings inculcates both theoretical as well as practical aspects of different areas like nature inspired algorithms, fuzzy systems, data mining, signal processing, image processing, text processing, wireless sensor networks, network security and cellular automata.
R 6,023.16
Pattern Recognition
This book constitutes the refereed proceedings of the 9th Mexican Conference on Pattern Recognition, MCPR 2017, held in Huatulco, Mexico, in June 2017. The 29 revised full papers presented were carefully reviewed and selected from 55 submissions. The papers are organized in topical sections on pattern recognition and artificial intelligence techniques, image processing and analysis, robotics and remote sensing, natural language processing and recognition, applications of pattern recognition.
R 2,177.58
Pattern Recognition
Pattern recognition is traditionally considered to cover all aspects of sensory data perception ranging from data acquisition, through preprocessing and low level analysis, to high level interpretation. Owing to its breadth and important application potential, the field of pattern recognition has been attracting considerable attention of researchers in academia and industry and consequently it has been witnessing a rapid growth and perpetual development. The need for dissemination of the latest results is being served by a host of international conferences on pattern recognition. One such series of meetings is regularly held in the United Kingdom under the auspices of the British Pattern Recognition Association. This volume contains papers presented at the BPRA 4th International Conference on Pattern Recognition held in Cambridge, March 28-30, 1988. Alongside the conventional topics of statistical and syntactic pattern recognition, contributions address issues in the hot subject areas of adaptive learning networks, computer vision, knowledge base methods and architectures for pattern processing, and among others, report progress in the application domains of document processing, speech and text recognition and shape analysis for industrial robotics. It is believed that the collection is not merely a report on current activities but that it will also be an important source of inspiration for future developments in the field of pattern recognition.
R 2,015.00
Pattern Recognition
This book constitutes the refereed proceedings of the 8th Mexican Conference on Pattern Recognition, MCPR 2016, held in Guanajuato, Mexico, in June 2016. The 34 revised full papers presented were carefully reviewed and selected from 60 submissions. The papers are organized in topical sections on computer vision and image analysis; pattern recognition and artificial intelligent techniques; signal processing and analysis; and applications of pattern recognition.
R 2,185.65
Advances in Intelligent Signal Processing and Data Mining
The book presents some of the most efficient statistical and deterministic methods for information processing and applications in order to extract targeted information and find hidden patterns. The techniques presented range from Bayesian approaches and their variations such as sequential Monte Carlo methods, Markov Chain Monte Carlo filters, Rao Blackwellization, to the biologically inspired paradigm of Neural Networks and decomposition techniques such as Empirical Mode Decomposition, Independent Component Analysis and Singular Spectrum Analysis. The book is directed to the research students, professors, researchers and practitioners interested in exploring the advanced techniques in intelligent signal processing and data mining paradigms.
R 6,191.00
Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition
For many engineering problems we require optimization processes with dynamic adaptation as we aim to establish the dimension of the search space where the optimum solution resides and develop robust techniques to avoid the local optima usually associated with multimodal problems. This book explores multidimensional particle swarm optimization, a technique developed by the authors that addresses these requirements in a well-defined algorithmic approach. After an introduction to the key optimization techniques, the authors introduce their unified framework and demonstrate its advantages in challenging application domains, focusing on the state of the art of multidimensional extensions such as global convergence in particle swarm optimization, dynamic data clustering, evolutionary neural networks, biomedical applications and personalized ECG classification, content-based image classification and retrieval, and evolutionary feature synthesis. The content is characterized by strong practical considerations, and the book is supported with fully documented source code for all applications presented, as well as many sample datasets. The book will be of benefit to researchers and practitioners working in the areas of machine intelligence, signal processing, pattern recognition, and data mining, or using principles from these areas in their application domains. It may also be used as a reference text for graduate courses on swarm optimization, data clustering and classification, content-based multimedia search, and biomedical signal processing applications.
R 4,030.00
On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling
A natural evolution of statistical signal processing, in connection with the progressive increase in computational power, has been exploiting higher-order information. Thus, high-order spectral analysis and nonlinear adaptive filtering have received the attention of many researchers. One of the most successful techniques for non-linear processing of data with complex non-Gaussian distributions is the independent component analysis mixture modelling (ICAMM). This thesis defines a novel formalism for pattern recognition and classification based on ICAMM, which unifies a certain number of pattern recognition tasks allowing generalization. The versatile and powerful framework developed in this work can deal with data obtained from quite different areas, such as image processing, impact-echo testing, cultural heritage, hypnograms analysis, web-mining and might therefore be employed to solve many different real-world problems.
R 3,023.23
Pattern Recognition
This book constitutes the proceedings of the 14th Mexican Conference on Pattern Recognition, MCPR 2022, which was held in planned to be held Ciudad Juárez, Mexico, in June 2022. The 33 papers presented in this volume were carefully reviewed and selected from 66 submissions. They are organized in the following topical sections: pattern recognition techniques; neural networks and deep learning; image and signal processing and analysis; natural language processing and recognition; robotics and remote sensing applications of pattern recognition; medical applications of pattern recognition.
R 3,332.94
Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition
For many engineering problems we require optimization processes with dynamic adaptation as we aim to establish the dimension of the search space where the optimum solution resides and develop robust techniques to avoid the local optima usually associated with multimodal problems. This book explores multidimensional particle swarm optimization, a technique developed by the authors that addresses these requirements in a well-defined algorithmic approach. After an introduction to the key optimization techniques, the authors introduce their unified framework and demonstrate its advantages in challenging application domains, focusing on the state of the art of multidimensional extensions such as global convergence in particle swarm optimization, dynamic data clustering, evolutionary neural networks, biomedical applications and personalized ECG classification, content-based image classification and retrieval, and evolutionary feature synthesis. The content is characterized by strong practical considerations, and the book is supported with fully documented source code for all applications presented, as well as many sample datasets. The book will be of benefit to researchers and practitioners working in the areas of machine intelligence, signal processing, pattern recognition, and data mining, or using principles from these areas in their application domains. It may also be used as a reference text for graduate courses on swarm optimization, data clustering and classification, content-based multimedia search, and biomedical signal processing applications.
R 4,030.00
On Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling
A natural evolution of statistical signal processing, in connection with the progressive increase in computational power, has been exploiting higher-order information. Thus, high-order spectral analysis and nonlinear adaptive filtering have received the attention of many researchers. One of the most successful techniques for non-linear processing of data with complex non-Gaussian distributions is the independent component analysis mixture modelling (ICAMM). This thesis defines a novel formalism for pattern recognition and classification based on ICAMM, which unifies a certain number of pattern recognition tasks allowing generalization. The versatile and powerful framework developed in this work can deal with data obtained from quite different areas, such as image processing, impact-echo testing, cultural heritage, hypnograms analysis, web-mining and might therefore be employed to solve many different real-world problems.
R 3,022.33
Pattern Recognition
This book constitutes the proceedings of the 11th Mexican Conference on Pattern Recognition, MCPR 2019, held in Querétaro, Mexico, in June 2019. The 40 papers presented in this volume were carefully reviewed and selected from 86 submissions. They were organized in topical sections named: artificial intelligence techniques and recognition; computer vision; industrial and medical applications of pattern recognition; image processing and analysis; pattern recognition techniques; signal processing and analysis; natural language, and processing and recognition.
R 2,199.59
Process Mining Techniques in Business Environments
After a brief presentation of the state of the art of process-mining techniques, Andrea Burratin proposes different scenarios for the deployment of process-mining projects, and in particular a characterization of companies in terms of their process awareness. The approaches proposed in this book belong to two different computational paradigms: first to classic "batch process mining," and second to more recent "online process mining."The book encompasses a revised version of the author's PhD thesis, which won the "Best Process Mining Dissertation Award" in 2014, awarded by the IEEE Task Force on Process Mining.
R 2,544.66
Pattern Recognition and Information Processing
This book constitutes the refereed proceedings of the 13th International Conference on Pattern Recognition and Information Processing, PRIP 2016, held in Minsk, Belarus, in October 2016. The 18 revised full papers presented were carefully reviewed and selected from 72 submissions. The papers are organized in topical sections on summarizing lectures; pattern recognition and image analysis; information processing and applications.
R 2,155.95
Pattern Recognition
This three-volume set LNCS 14406-14408 constitutes the refereed proceedings of the 7th Asian Conference on Pattern Recognition, ACPR 2023, held in Kitakyushu, Japan, in November 2023. The 93 full papers presented were carefully reviewed and selected from 164 submissions. The conference focuses on four important areas of pattern recognition: pattern recognition and machine learning, computer vision and robot vision, signal processing, and media processing and interaction, covering various technical aspects.
R 3,534.48
Pattern Recognition
This 2-volume set LNCS 15297-15298 constitutes the refereed proceedings of the 46th Annual Conference of the German Association for Pattern Recognition, DAGM-GCPR 2024, held in Munich, Germany, during September 10-13, 2024.The 44 full papers included in these proceedings were carefully reviewed and selected from 81 submissions. They are organized in these topical sections: Part I: Clustering and Segmentation; Learning Techniques; Medical and Biological Applications; Uncertainty and Explainability.Part II: Modelling of Faces and Shapes; Image Generation and Reconstruction; 3D Analysis and Sythesis; Video Analysis; Photogrammetry and Remote Sensing.
R 2,950.70
Process Mining Techniques for Managing and Improving Healthcare Systems
This book discusses a new process mining method along with a detailed comparison between different techniques that provide a complete vision of the process of data acquisition, data analysis, and data prediction.Process Mining Techniques for Managing and Improving Healthcare Systems offers a new framework for process learning which is probabilistic and enables the process to be learned in an accumulative manner. The steps of prediction modeling and building the required knowledge are highlighted throughout the book along with a strong emphasis on the correlation between the healthcare domain and technology including the different aspects such as managing records, information, and procedures, early detection of diseases, and the improvement of accuracy in choosing the right treatment procedures.This reference provides a wealth of knowledge for practitioners, researchers, and students at basic and intermediary levels working within the healthcare system, computer science, electronics and communications, as well as medical providers, and also hospital management entities.
R 9,305.71
Pattern Recognition
This book constitutes the proceedings of the 17th Mexican Conference on Pattern Recognition, MCPR 2025, held in Guanajuato, Mexico, during June 25–28, 2025.The 36 full papers presented in this volume were carefully reviewed and selected from 70 submissions. They are grouped into the following topics: Pattern Recognition and Machine Learning Techniques; Language Processing and Recognition; Computer Vision; Medical Applications of Pattern Recognition; Reinforcement Learning; Deep Learning and Neural Networks.
R 5,437.14
Pattern Recognition Theory and Application
Research in the field of pattern recognition both in theo retical terms and in the area of appl ication continues to flourish. Pattern recognition is a fairly diverse field involving researchers whose primary disciplines spread over at least a half dozen fields. Possibly because of the great diversity of backgrounds but a common interest in certain broad areas of application, the field has grown so rapidly and yet seems to promise at least a similar growth rate for the future. This book is a collection containing some of the papers that were presented at the N. A. T. O. Advanced Study Institute held in Bandol, France, September 1975. The main purpose of the institute was to present material which would provide a basic background in the field. Thus, survey papers covering syntactic methods, picture processing, classification theory, and speech recognition were presented. This should have provided the listener (and we hope now, the reader) with an acquaintance with the basic tools, a look at some of the appl ications and an appraisal of how each of the particular topics will evolve. A more recent addition to the pattern recognition "family" is the work in the areas of economics and group choice. Since the process of recognizing and inter preting patterns is so fundamental, it probably is no surprise when a particular discipline is discovered to be amenable to the already developed techniques.
R 1,633.42
Statistical Pattern Recognition
Statistical pattern recognition relates to the use of statistical techniques for analysing data measurements in order to extract information and make justified decisions. It is a very active area of study and research, which has seen many advances in recent years. Applications such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition, all require robust and efficient pattern recognition techniques. This third edition provides an introduction to statistical pattern theory and techniques, with material drawn from a wide range of fields, including the areas of engineering, statistics, computer science and the social sciences. The book has been updated to cover new methods and applications, and includes a wide range of techniques such as Bayesian methods, neural networks, support vector machines, feature selection and feature reduction techniques.Technical descriptions and motivations are provided, and the techniques are illustrated using real examples. Statistical Pattern Recognition, 3rd Edition: Provides a self-contained introduction to statistical pattern recognition.Includes new material presenting the analysis of complex networks.Introduces readers to methods for Bayesian density estimation.Presents descriptions of new applications in biometrics, security, finance and condition monitoring.Provides descriptions and guidance for implementing techniques, which will be invaluable to software engineers and developers seeking to develop real applicationsDescribes mathematically the range of statistical pattern recognition techniques.Presents a variety of exercises including more extensive computer projects. The in-depth technical descriptions make the book suitable for senior undergraduate and graduate students in statistics, computer science and engineering. Statistical Pattern Recognition is also an excellent reference source for technical professionals. Chapters have been arranged to facilitate implementation of the techniques by software engineers and developers in non-statistical engineering fields. www.wiley.com/go/statisticalpatternrecognition
R 2,799.00
Pattern Recognition and Machine Intelligence
This book constitutes the refereed proceedings of the Second International Conference on Pattern Recognition and Machine Intelligence, PReMI 2007, held in Kolkata, India in December 2007. The papers are organized in topical sections on pattern recognition, image analysis, soft computing and applications, data mining and knowledge discovery, bioinformatics, signal and speech processing, document analysis and text mining, biometrics, and video analysis.
R 4,338.03
Progress in Artificial Intelligence and Pattern Recognition
This book constitutes the refereed proceedings of the 6th International Workshop on Artificial Intelligence and Pattern Recognition, IWAIPR 2018, held in Havana, Cuba, in September 2018. The 42 full papers presented were carefully reviewed and selected from 101 submissions. The papers promote and disseminate ongoing research on mathematical methods and computing techniques for artificial intelligence and pattern recognition, in particular in bioinformatics, cognitive and humanoid vision, computer vision, image analysis and intelligent data analysis, as well as their application in a number of diverse areas such as industry, health, robotics, data mining, opinion mining and sentiment analysis, telecommunications, document analysis, and natural language processing and recognition.
R 2,190.48
Machine Learning and Data Mining in Pattern Recognition
This book constitutes the refereed proceedings of the 12th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2016, held in New York, NY, USA in July 2016. The 58 regular papers presented in this book were carefully reviewed and selected from 169 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multimedia data types such as image mining, text mining, video mining and Web mining.
R 3,122.70
Proceedings of International Conference on Cognition and Recognition
The book covers a comprehensive overview of the theory, methods, applications and tools of cognition and recognition. The book is a collection of best selected papers presented in the International Conference on Cognition and Recognition 2016 (ICCR 2016) and helpful for scientists and researchers in the field of image processing, pattern recognition and computer vision for advance studies. Nowadays, researchers are working in interdisciplinary areas and the proceedings of ICCR 2016 plays a major role to accumulate those significant works at one place. The chapters included in the proceedings inculcates both theoretical as well as practical aspects of different areas like nature inspired algorithms, fuzzy systems, data mining, signal processing, image processing, text processing, wireless sensor networks, network security and cellular automata.
R 6,024.06
Pattern Recognition And Big Data
Containing twenty six contributions by experts from all over the world, this book presents both research and review material describing the evolution and recent developments of various pattern recognition methodologies, ranging from statistical, linguistic, fuzzy-set-theoretic, neural, evolutionary computing and rough-set-theoretic to hybrid soft computing, with significant real-life applications.Pattern Recognition and Big Data provides state-of-the-art classical and modern approaches to pattern recognition and mining, with extensive real life applications. The book describes efficient soft and robust machine learning algorithms and granular computing techniques for data mining and knowledge discovery; and the issues associated with handling Big Data. Application domains considered include bioinformatics, cognitive machines (or machine mind developments), biometrics, computer vision, the e-nose, remote sensing and social network analysis.
R 12,518.97
Machine Learning and Data Mining in Pattern Recognition
This two-volume set LNAI 10934 and LNAI 10935 constitutes the refereed proceedings of the 14th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2018, held in New York, NY, USA in July 2018. The 92 regular papers presented in this two-volume set were carefully reviewed and selected from 298 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multi-media data types such as image mining, text mining, video mining, and Web mining.
R 1,583.84
Applications and Developments in Semantic Process Mining
As technology becomes increasingly intelligent, various factors within the field of data science are seeing significant transformation. Process analysis is one area that is undergoing substantial development due to the implementation of semantic reasoning and web technologies. The congruence of these two systems has created various applications and developments in data processing and analysis across several professional fields. Applications and Developments in Semantic Process Mining is an essential reference source that discusses the improvement of process mining algorithms through the implementation of semantic modeling and representation. Featuring research on topics such as domain ontologies, fuzzy modeling, and information extraction, the book takes into account the different stages of process mining and its application in real time and then expounds the classical process mining techniques to semantical preparation of the extracted models for further analysis and querying at a more abstract level. The book provides a wide-ranging idea of the application and development of semantic process mining that is expected to be beneficial and used by professionals, software and data engineers, software developers, IT experts, business owners and entrepreneurs, and process analysts.
R 8,313.20
Evolutionary Synthesis of Pattern Recognition Systems
Integrates computer vision, pattern recognition, and AI.Presents original research that will benefit researchers and professionals in computer vision, pattern recognition, target recognition, machine learning, evolutionary learning, image processing, knowledge discovery and data mining, cybernetics, robotics, automation and psychology
R 4,656.62
Advances in Intelligent Signal Processing and Data Mining
The book presents some of the most efficient statistical and deterministic methods for information processing and applications in order to extract targeted information and find hidden patterns. The techniques presented range from Bayesian approaches and their variations such as sequential Monte Carlo methods, Markov Chain Monte Carlo filters, Rao Blackwellization, to the biologically inspired paradigm of Neural Networks and decomposition techniques such as Empirical Mode Decomposition, Independent Component Analysis and Singular Spectrum Analysis. The book is directed to the research students, professors, researchers and practitioners interested in exploring the advanced techniques in intelligent signal processing and data mining paradigms.
R 7,352.51
Machine Learning and Data Mining in Pattern Recognition
This book constitutes the refereed proceedings of the 7th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2011, held in New York, NY, USA.The 44 revised full papers presented were carefully reviewed and selected from 170 submissions. The papers are organized in topical sections on classification and decision theory, theory of learning, clustering, application in medicine, webmining and information mining; and machine learning and image mining.
R 1,609.42
