Pattern Recognition and Machine Learning

Description

Pattern recognition has its origins in engineering, whereas machine learning grew out of computer science. However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory. Pattern Recognition and Machine Learning_Christopher M. Bishop (Springer 2006 703s)......Page 1 Information Science and Statistics......Page 2 Pattern Recognition and Machine Learning......Page 3 Preface......Page 6 Mathematical notation......Page 9 Contents......Page 11 1 Introduction......Page 19 2 Probability Distributions......Page 85 3 Linear Models for Regression......Page 155 4 Linear Models for Classification......Page 196 5 Neural Networks......Page 242 6 Kernel Methods......Page 308 7 Sparse Kernel Machines......Page 341 8 Graphical Models......Page 375 9 Mixture Models and EM......Page 439 10 Approximate Inference......Page 476 11 Sampling Methods......Page 538 13 Sequential Data......Page 574 14 Combining Models......Page 622 Appendix A. Data Sets......Page 646 Appendix B. Probability Distributions......Page 653 Appendix C. Properties of Matrices......Page 662 Appendix D. Calculus of Variations......Page 669 Appendix E. Lagrange Multipliers......Page 672 References......Page 676 Index......Page 694 Pattern Recognition and Machine Learning (Solutions to the Exercises 2007 100s) _Christopher M. Bishop......Page 704 Contents......Page 708 Chapter 1 Pattern Recognition......Page 710 Chapter 2 Density Estimation......Page 722 Chapter 3 Linear Models for Regression......Page 737 Chapter 4 Linear Models for Classification......Page 744 Chapter 5 Neural Networks......Page 749 Chapter 6 Kernel Methods......Page 756 Chapter 7 Sparse Kernel Machines......Page 762 Chapter 8 Probabilistic Graphical Models......Page 766 Chapter 9 Mixture Models......Page 771 Chapter 10 Variational Inference and EM......Page 775 Chapter 11 Sampling Methods......Page 785 Chapter 12 Latent Variables......Page 787 Chapter 13 Sequential Data......Page 794 Chapter 14 Combining Models......Page 798