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Pattern classification and scene analysis
482 pages
A comprehensive, foundational journey through the mathematical logic that teaches machines how to perceive the world.
What it feels like
Reading this book feels like stepping into a highly structured, intellectual workshop where the building blocks of artificial perception are laid out with clarity and purpose. The pace is steady and deliberate, guiding the reader through complex statistical theories and computational logic without getting bogged down in overly rigid mathematical formalism. Instead, the mood is pragmatic and focused, driven by a desire to provide genuine insight into how machines can be taught to recognize patterns, speech, and visual scenes. The prose is instructive and balanced, relying on illustrative examples to bridge the gap between abstract theory and real-world application. It is a deeply analytical experience, perfect for computer scientists and mathematicians who delight in understanding the foundational mechanics of machine learning and artificial intelligence.
- Mood
- intellectual
- structured
- focused
- Tone
- academic
- instructive
- analytical
- pragmatic
- Pace
- steady
- Themes
- theoretical foundations · technological application · problem-solving · computational logic
- The writing
- Clear, balanced, and mathematically grounded, prioritizing insight and practical understanding over rigid formalism.
- The arc
- Moves from foundational theoretical principles to complex, real-world applications of machine perception.
- Who it's for
- Students, researchers, and computer scientists seeking a comprehensive, foundational understanding of machine learning and computer vision.
About the book
From the inside cover: Here is a unified, Comprehensive, and up–to–date treatment of the theoretical principles of pattern recognition. These principles are applicable to a great variety of problems of current interest, such as character recognition, speech recognition, speaker identification, fingerprint recognition, the analysis of biomedical photographs, aerial photoreconnaissance, automatic inspection for industrial quality control, and visual systems for robots. Throughout Pattern Classification and Scene Analysis, the authors have balanced their presentation to reflect the relative importance of the many theoretical topics in the field. Pattern Classification and Scene Analysis is the first book to provide comprehensive coverage of both statistical classification theory and computer analysis of pictures. Part I covers Bayesian decision theory, supervised and unsupervised learning, nonparametric techniques, discriminant analysis, and clustering. Part II describes many techniques of current interest in automatic scene analysis, including preprocessing of pictorial data, spatial filtering, shape–description techniques, perspective transformations, projective invariants, linguistic procedures, and artificial intelligence techniques for scene analysis. Although the theories and techniques of pattern recognition are largely mathematical, the authors have been more concerned with providing insight and understanding than with establishing rigorous mathematical foundations. The many illustrative examples, plausibility arguments, and discussions of the behavior of solutions reflect this concern. Extensive bibliographical and historical remarks at the end of each chapter further enhance the presentation. Standard notation is used wherever possible, and a comprehensive index is included. Typical first–year graduate students will find most of the mathematical arguments well within their grasp. Because the exposition is clear and balanced, Pattern Classification and Scene Analysis is suitable for both college and professional use. In particular, it will appeal to graduate students and professionals in the fields of computer science, electrical engineering, and statistics. Students and professionals in psychology, biomedical science, meteorology, and biology will also find it of value for the light it sheds on such areas as visual perception, image processing, and numerical taxonomy
Description from Open Library.
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