Essentials of Pattern Recognition: An Accessible Approach

ISBN-13: 9781108483469 / Angielski / Twarda / 2020 / 398 str.

China) Jianxin Wu (Nanjing University

Opis

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.

Recenzje

'This well-designed book is both accessible and substantial in content. I highly recommend it as a textbook as well as for self-study.' Zhi-Hua Zhou, Nanjing University

Spis treści

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.