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Features of particular interest include:    - A NETLAB toolbox which is freely available - Worked examples, demonstration programs and over 100 graded exercises - Cutting edge research made accessible for the first time in a highly usable form - Comprehensive coverage of visualisation methods, Bayesian techniques for neural networks and Gaussian Processes    Although primarily a textbook for teaching undergraduate and postgraduate courses in pattern recognition and neural networks, this book will also be of interest to practitioners and researchers who can use the toolbox to develop application solutions and new models.    \"...provides a unique collection of many of the most important pattern recognition algorithms. With its use of compact and easily modified MATLAB scripts, the book is ideally suited to both teaching and research.\" Christopher Bishop, Microsoft Research, Cambridge, UK    \"...a welcome addition to the literature on neural networks and how to train and use them to solve many of the statistical problems that occur in data analysis and data mining\" Jack Cowan, Mathematics Department, University of Chicago, US    \"If you have a pattern recognition problem, you should consider NETLAB; if you use NETLAB you must have this book.\" Keith Worden, University of Sheffield, UK","brand":"WoB","offers":[{"title":"- \/ - \/ -","offer_id":55070980079989,"sku":"","price":0.0,"currency_code":"GBP","in_stock":true},{"title":"US \/ NEW \/ INGRAM","offer_id":55070980374901,"sku":"NIN9781852334406","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0810\/6996\/5613\/files\/B0082PXYCQ.jpg?v=1739634858"},{"product_id":"probabilistic-graphical-models-book-luis-enrique-sucar-9783030619459","title":"Probabilistic Graphical Models","description":"This fully updated new edition of a uniquely accessible textbook\/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective.  It features new material on partially observable Markov decision processes, causal graphical models, causal discovery and deep learning, as well as an even greater number of exercises; it also incorporates a software library for several graphical models in Python.  The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and learning principles, and reviews real-world applications for each type of model. These applications are drawn from a broad range of disciplines, highlighting the many uses of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams, and Markov decision processes.  Topics and features:    Presents a unified framework encompassing all of the main classes of PGMs Explores the fundamental aspects of representation, inference and learning for each technique Examines new material on partially observable Markov decision processes, and graphical models Includes a new chapter introducing deep neural networks and their relation with probabilistic graphical models  Covers multidimensional Bayesian classifiers, relational graphical models, and causal models  Provides substantial chapter-ending exercises, suggestions for further reading, and ideas for research or programming projects Describes classifiers such as Gaussian Naive Bayes, Circular Chain Classifiers, and Hierarchical Classifiers with Bayesian Networks Outlines the practical application of the different techniques Suggests possible course outlines for instructors  This classroom-tested work is suitable as a textbook for an advanced undergraduate or a graduate course in probabilistic graphical models for students of computer science, engineering, and physics. Professionals wishing to apply probabilistic graphical models in their own field, or interested in the basis of these techniques, will also find the book to be an invaluable reference.  Dr. Luis Enrique Sucar is a Senior Research Scientist at the National Institute for Astrophysics, Optics and Electronics (INAOE), Puebla, Mexico. He received the National Science Prize en 2016.","brand":"WoB","offers":[{"title":"- \/ - \/ -","offer_id":55085954335093,"sku":"","price":0.0,"currency_code":"GBP","in_stock":true},{"title":"US \/ NEW \/ INGRAM","offer_id":55085954531701,"sku":"NIN9783030619459","price":0.0,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0810\/6996\/5613\/files\/3030619451.jpg?v=1739761774"},{"product_id":"smart-information-systems-book-frank-hopfgartner-9783319141770","title":"Smart Information Systems","description":"This text presents an overview of smart information systems for both the private and public sector, highlighting the research questions that can be studied by applying computational intelligence. 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With an emphasis on both high-level concepts, and practical detail, the text links theory, algorithms, and issues of hardware and software implementation in intelligent vehicle research. Topics and features: presents a thorough introduction to the development and latest progress in intelligent vehicle research, and proposes a basic framework; provides detection and tracking algorithms for structured and unstructured roads, as well as on-road vehicle detection and tracking algorithms using boosted Gabor features; discusses an approach for multiple sensor-based multiple-object tracking, in addition to an integrated DGPS\/IMU positioning approach; examines a vehicle navigation approach using global views; introduces algorithms for lateral and longitudinal vehicle motion control.","brand":"WoB","offers":[{"title":"GB \/ NEW \/ INGRAM","offer_id":55613184639349,"sku":"NLS9781447122791","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0810\/6996\/5613\/files\/9781447122791.jpg?v=1752231904"},{"product_id":"fundamentals-of-computerized-tomography-book-gabor-t-herman-9781447125211","title":"Fundamentals of Computerized Tomography","description":"This revised and updated text presents the computational and mathematical procedures underlying data collection, image reconstruction, and image display in computerized tomography. 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An excellent guide for practitioners, it can also serve as a textbook for an introductory graduate course.","brand":"WoB","offers":[{"title":"- \/ - \/ INTERNAL","offer_id":55613454287221,"sku":null,"price":0.0,"currency_code":"GBP","in_stock":true},{"title":"GB \/ NEW \/ INGRAM","offer_id":55613454549365,"sku":"NLS9781447125211","price":0.0,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0810\/6996\/5613\/files\/9781447125211.jpg?v=1752233823"},{"product_id":"support-vector-machines-for-pattern-classification-book-shigeo-abe-9781447125488","title":"Support Vector Machines for Pattern Classification","description":"A guide on the use of SVMs in pattern classification, including a rigorous performance comparison of classifiers and regressors. The book presents architectures for multiclass classification and function approximation problems, as well as evaluation criteria for classifiers and regressors. 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