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Pattern Classification
The first edition, published in 1973, has become a classicreference in the field. Now with the second edition, readers willfind information on key new topics such as neural networks andstatistical pattern recognition, the theory of machine learning,and the theory of invariances. Also included are worked examples,comparisons between different methods, extensive graphics, expandedexercises and computer project topics. An Instructor's Manual presenting detailed solutions to all theproblems in the book is available from the Wiley editorialdepartment.
Patterns, Predictions, and Actions: Foundations of Machine Learning
An authoritative, up-to-date graduate textbook on machine learning that highlights its historical context and societal impacts Patterns, Predictions, and Actions introduces graduate students to the essentials of machine learning while offering invaluable perspective on its history and social implications. Beginning with the foundations of decision making, Moritz Hardt and Benjamin Recht explain how representation, optimization, and generalization are the constituents of supervised learning. They go on to provide self-contained discussions of causality, the practice of causal inference, sequential decision making, and reinforcement learning, equipping readers with the concepts and tools they ...
Understanding Machine Learning
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Chosen for the Marriage Bed
The Welsh Marches 1460 In the eerie depths of Llanwardine Priory, Elizabeth de Lacy is about to take the veil when she is told she must wed her family’s sworn enemy! Lord Richard Malinder must produce an heir, and a union with the de Lacy family could prove advantageous – if only to keep his enemies close...
Reinforcement Learning, second edition
The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence. Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms. This second edition has been significantly expanded and updated, presenting new topics and updating coverage of other topics. Like the fir...
The Steel Remains
Ringil, the hero of the bloody slaughter at Gallows Gap is a legend to all who don't know him and a twisted degenerate to those that do. A veteran of the wars against the lizards he makes a living from telling credulous travellers of his exploits. Until one day he is pulled away from his life and into the depths of the Empire's slave trade. Where he will discover a secret infinitely more frightening than the trade in lives. Archeth - pragmatist, cynic and engineer, the last of her race - is called from her work at the whim of the most powerful man in the Empire and sent to its farthest reaches to investigate a demonic incursion against the Empire's borders. Egar Dragonbane, steppe-nomad, one...
Pattern Classification and Scene Analysis
Introduction to Mathematical Techniques in Pattern Recognition by Harry C. Andrews This volume is one of the first cohesive treatments of the use of mathematics for studying interactions between various recognition environments. It brings together techniques previously scattered throughout the literature and provides a concise common notation that will facilitate the understanding and comparison of the many aspects of mathematical pattern recognition. The contents of this volume are divided into five interrelated subject areas: Feature Selection, Distribution Free Classification, Statistical Classification, Nonsupervised Learning, and Sequential Learning. Appendices describing specific aspec...
Multiple View Geometry in Computer Vision
A basic problem in computer vision is to understand the structure of a real world scene given several images of it. Techniques for solving this problem are taken from projective geometry and photogrammetry. Here, the authors cover the geometric principles and their algebraic representation in terms of camera projection matrices, the fundamental matrix and the trifocal tensor. The theory and methods of computation of these entities are discussed with real examples, as is their use in the reconstruction of scenes from multiple images. The new edition features an extended introduction covering the key ideas in the book (which itself has been updated with additional examples and appendices) and significant new results which have appeared since the first edition. Comprehensive background material is provided, so readers familiar with linear algebra and basic numerical methods can understand the projective geometry and estimation algorithms presented, and implement the algorithms directly from the book.
Handbook of Ring-Opening Polymerization
This comprehensive, truly one-stop reference discusses monomers, methods, stereochemistry, industrial applications and more. Chapters written by internationally acclaimed experts in their respective fields cover both basic principles and up-to-date information, ranging from the controlled ring-opening polymerization methods to polymer materials of industrial interest. All main classes of monomers including heterocyclics, cyclic olefins and alkynes, and cycloalkanes, are discussed separately as well as their specificities regarding the ring-opening polymerization techniques, the mechanisms, the degree of control, the properties of the related polymers and their applications. The two last chapters are devoted to the implementation of green chemistry in ring-opening polymerization processes. Of much interest to chemists in academia and industry.
Digital Image Processing and Pattern Recognition
This book is designed for undergraduate and postgraduate students of Computer Science and Engineering, Information Technology, Electronics and Communication Engineering, and Electrical Engineering. The book comprehensively covers all the important topics in digital image processing and pattern recognition along with the fundamental concepts, mathematical preliminaries and theoretical derivations of significant theorems. The image processing topics include coverage of image formation, digitization, lower level processing, image analysis, image compression, and so on. The topics on pattern recognition include statistical decision making, decision tree learning, artificial neural networks, clus...
