Essentials of Pattern Recognition
Cambridge University Press (Verlag)
978-1-108-48346-9 (ISBN)
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.
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.
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.
| Erscheinungsdatum | 20.11.2020 |
|---|---|
| Zusatzinfo | Worked examples or Exercises |
| Verlagsort | Cambridge |
| Sprache | englisch |
| Maße | 174 x 250 mm |
| Gewicht | 960 g |
| Themenwelt | Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik |
| ISBN-10 | 1-108-48346-1 / 1108483461 |
| ISBN-13 | 978-1-108-48346-9 / 9781108483469 |
| Zustand | Neuware |
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