Essentials of Pattern Recognition: An Accessible Approach
Cambridge University Press, 11/19/2020
EAN 9781108483469, ISBN10: 1108483461
Hardcover, 398 pages, 24.8 x 17.1 x 2.5 cm
Language: English
Originally published in English
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.
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.