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The cover of “Gaussian processes for machine learning” - Open Library.

"Gaussian processes for machine learning" was published by MIT Press in 2006 - Cambridge, Mass, it has 272 pages and the language of the book is English.


“Gaussian processes for machine learning” Metadata:

  • Title: ➤  Gaussian processes for machine learning
  • Author:
  • Language: English
  • Number of Pages: 272
  • Publisher: MIT Press
  • Publish Date:
  • Publish Location: Cambridge, Mass

“Gaussian processes for machine learning” Subjects and Themes:

Edition Specifications:

  • Pagination: p. cm.

Edition Identifiers:

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"Gaussian processes for machine learning" Table Of Contents:

  • 1- . Table of Contents
  • 2- . Series Foreword
  • 3- . Preface
  • 4- . Symbols and Notation
  • 5- . 1: Introduction
  • 6- A Pictorial Introduction to Bayesian Modelling
  • 7- Roadmap
  • 8- . 2: Regression
  • 9- Weight-space View
  • 10- Function-space View
  • 11- Varying the Hyperparameters
  • 12- Decision Theory for Regression
  • 13- An Example Application
  • 14- Smoothing, Weight Functions and Equivalent Kernels
  • 15- History and Related Work
  • 16- Appendix: Infinite Radial Basis Function Networks
  • 17- Exercises
  • 18- . 3 Classification
  • 19- Classification Problems
  • 20- Linear Models for Classification
  • 21- Gaussian Process Classification
  • 22- The Laplace Approximation for the Binary GP Classifier
  • 23- Multi-class Laplace Approximation
  • 24- Expectation Propagation
  • 25- Experiments
  • 26- Discussion
  • 27- Appendix: Moment Derivations
  • 28- Exercises
  • 29- . 4 Covariance Functions
  • 30- Preliminaries
  • 31- Examples of Covariance Functions
  • 32- Eigenfunction Analysis of Kernels
  • 33- Kernels for Non-vectorial Inputs
  • 34- Exercises
  • 35- . 5 Model Selection and Adaptation of Hyperparameters
  • 36- 5.1 The Model Selection Problem
  • 37- 5.2 Bayesian Model Selection
  • 38- 5.3 Cross-validation
  • 39- 5.4 Model Selection for GP Regression
  • 40- 5.5 Model Selection for GP Classification
  • 41- 5.6 Exercises
  • 42- . 6 Relationships between GPs and Other Models
  • 43- 6.1 Reproducing Kernel Hilbert Spaces
  • 44- 6.2 Regularization
  • 45- 6.3 Spline Models
  • 46- 6.4 Support Vector Machines
  • 47- 6.5 Least-Squares Classification
  • 48- 6.6 Relevance Vector Machines
  • 49- 6.7 Exercises
  • 50- . 7 Theoretical Perspectives
  • 51- 7.1 The Equivalent Kernel
  • 52- 7.2 Asymptotic Analysis
  • 53- 7.3 Average-case Learning Curves
  • 54- 7.4 PAC-Bayesian Analysis
  • 55- 7.5 Comparison with Other Supervised Learning Methods
  • 56- 7.6 Appendix: Learning Curve for the Ornstein-Uhlenbeck Process
  • 57- 7.7 Exercises
  • 58- . 8 Approximation Methods for Large Datasets
  • 59- 8.1 Reduced-rank Approximations of the Gram Matrix
  • 60- 8.2 Greedy Approximation
  • 61- 8.3 Approximations for GPR with Fixed Hyperparameters
  • 62- 8.4 Approximations for GPC with Fixed Hyperparameters
  • 63- 8.5 Approximating the Marginal Likelihood and its Derivatives
  • 64- 8.6 Appendix: Equivalence of SR and GPR using the Nyström Approximate Kernel
  • 65- 8.7 Exercises
  • 66- . 9 Further Issues and Conclusions
  • 67- 9.1 Multiple Outputs
  • 68- 9.2 Noise Models with Dependencies
  • 69- 9.3 Non-Gaussian Likelihoods
  • 70- 9.4 Derivative Observations
  • 71- 9.5 Prediction with Uncertain Inputs
  • 72- 9.6 Mixtures of Gaussian Processes
  • 73- 9.7 Global Optimization
  • 74- 9.8 Evaluation of Integrals
  • 75- 9.9 Student's t Process
  • 76- 9.10 Invariances
  • 77- 9.11 Latent Variable Models
  • 78- 9.12 Conclusions and Future Directions
  • 79- . A Mathematical Background
  • 80- . B Gaussian Markov Processes
  • 81- . C Datasets and Code
  • 82- . Bibliography
  • 83- . Author Index
  • 84- . Subject Index

"Gaussian processes for machine learning" Description:

The Open Library:

Gaussian processes (GPs) provide an approach to kernel-machine learning. This book provides a treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. (From the book's web site, http://www.gaussianprocess.org/gpml/ )

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