Introduces fundamental concepts, major models, and popular applications of pattern recognition for a one-semester undergraduate course. The text focuses on a relatively small number of core concepts with an abundance of illustrations and examples and provides extensive practice through student exercises.
Introduces fundamental concepts, major models, and popular applications of pattern recognition for a one-semester undergraduate course. The text focuses on a relatively small number of core concepts with an abundance of illustrations and examples and provides extensive practice through student exercises.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Jianxin Wu is a professor in the Department of Computer Science and Technology and the School of Artificial Intelligence at Nanjing University, China. He received his B.S. and M.S. degrees in computer science from Nanjing University and his Ph.D. degree in computer science from the Georgia Institute of Technology. Professor Wu has served as an area chair for the conference on Computer Vision and Pattern Recognition (CVPR), the International Conference on Computer Vision (ICCV), and the AAAI Conference on Artificial Intelligence, and he is an associate editor for the Pattern Recognition journal. His research interests are computer vision and machine learning.
Inhaltsangabe
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.
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.
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