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ESSENTIALS OF PATTERN RECOGNITION. AN ACCESSIBLE APPROACH
Título:
ESSENTIALS OF PATTERN RECOGNITION. AN ACCESSIBLE APPROACH
Subtítulo:
Autor:
WU, J
Editorial:
CAMBRIDGE UNIVERSITY PRESS
Año de edición:
2020
Materia
INGENIERIA DEL SOFTWARE - OTROS TEMAS
ISBN:
978-1-108-48346-9
Páginas:
398
80,60 €

 

Sinopsis

This textbook introduces fundamental concepts, major models, and popular applications of pattern recognition for a one-semester undergraduate course. To ensure student understanding, the text focuses on a relatively small number of core concepts with an abundance of illustrations and examples. Concepts are reinforced with hands-on exercises to nurture the student´s skill in problem solving. New concepts and algorithms are framed by real-world context and established as part of the big picture introduced in an early chapter. A problem-solving strategy is employed in several chapters to equip students with an approach for new problems in pattern recognition. This text also points out common errors that a new player in pattern recognition may encounter, and fosters the ability for readers to find useful resources and independently solve a new pattern recognition task through various working examples. Students with an undergraduate understanding of mathematical analysis, linear algebra, and probability will be well prepared to master the concepts and mathematical analysis presented here.

Focuses on core concepts to ensure mastery of the fundamentals
Presents a strategy for problem-solving so that students can solve unfamiliar problems
Features an abundance of thought-provoking real-world issues and exercises to help students connect theory with practice
Patient, step-by-step explication of algorithms so that students understand which to apply in which situation



Table of Contents

Preface
Notation
Part I. Introduction and Overview:
1. Introduction
2. Mathematical background
3. Overview of a pattern recognition system
4. Evaluation
Part II. Domain-Independent Feature Extraction:
5. Principal component analysis
6. Fisher´s linear discriminant
Part III. Classifiers and Tools:
7. Support vector machines
8. Probabilistic methods
9. Distance metrics and data transformations
10. Information theory and decision trees
Part IV. Handling Diverse Data Formats:
11. Sparse and misaligned data
12. Hidden Markov model
Part V. Advanced Topics:
13. The normal distribution
14. The basic idea behind expectation-maximization
15. Convolutional neural networks
References
Index.