2001-an-introduction-to-kernel-based-learning-algorithms.md

title	authors	fieldsOfStudy	meta_key	numCitedBy	reading_status	ref_count	tags	urls	venue	year
An introduction to kernel-based learning algorithms	K. Müller S. Mika Gunnar Rätsch K. Tsuda B. Schölkopf	Computer Science	2001-an-introduction-to-kernel-based-learning-algorithms	3524	TBD	216	gen-from-ref other-default paper	https://www.semanticscholar.org/paper/An-introduction-to-kernel-based-learning-algorithms-Müller-Mika/1fcbefeb0beae4470cf40df74cd116b1d4bdcae4?sort=total-citations	IEEE Trans. Neural Networks	2001

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An introduction to kernel-based learning algorithms

Abstract

This paper provides an introduction to support vector machines, kernel Fisher discriminant analysis, and kernel principal component analysis, as examples for successful kernel-based learning methods. We first give a short background about Vapnik-Chervonenkis theory and kernel feature spaces and then proceed to kernel based learning in supervised and unsupervised scenarios including practical and algorithmic considerations. We illustrate the usefulness of kernel algorithms by discussing applications such as optical character recognition and DNA analysis.

Paper References

1. A Mathematical Programming Approach to the Kernel Fisher Algorithm

2. Invariant Feature Extraction and Classification in Kernel Spaces

3. Generalized Discriminant Analysis Using a Kernel Approach

4. An improved training algorithm for kernel Fisher discriminants

5. On a Kernel-Based Method for Pattern Recognition, Regression, Approximation, and Operator Inversion

6. Nonlinear Discriminant Analysis Using Kernel Functions

7. Fisher discriminant analysis with kernels

8. Sparse Kernel Feature Analysis

9. The Kernel-Adatron Algorithm - A Fast and Simple Learning Procedure for Support Vector Machines

10. Kernel Methods for Extracting Local Image Semantics

11. An introduction to Support Vector Machines

12. Nonlinear Component Analysis as a Kernel Eigenvalue Problem

13. Advances in kernel methods - support vector learning

14. Sparse Kernel Principal Component Analysis

15. The connection between regularization operators and support vector kernels

16. Kernel PCA and De-Noising in Feature Spaces

17. Support vector learning

18. Convolution kernels on discrete structures

19. The Relevance Vector Machine

20. New Support Vector Algorithms

21. Pattern classification - a unified view of statistical and neural approaches

22. Kernel PCA for Feature Extraction and De-Noising in Nonlinear Regression

23. Kernel-dependent support vector error bounds

24. Training support vector machines - an application to face detection

25. Generalization performance of regularization networks and support vector machines via entropy numbers of compact operators

26. Statistical Pattern Recognition

27. Support Vector Machine Reference Manual

28. Adaptive Margin Support Vector Machines

29. Input space versus feature space in kernel-based methods

30. SVM and Boosting - One Class

31. Support vector machines for spam categorization

32. A training algorithm for optimal margin classifiers

33. An Equivalence Between Sparse Approximation and Support Vector Machines

34. Optimal hyperplane classifier based on entropy number bound

35. Knowledge-based analysis of microarray gene expression data by using support vector machines.

36. Text Categorization with Support Vector Machines - Learning with Many Relevant Features

37. Simplified Support Vector Decision Rules

38. Support Vector Method for Novelty Detection

39. Support Vector Machines - Training and Applications

40. Neural networks for pattern recognition

41. Dynamic Alignment Kernels

42. Making large scale SVM learning practical

43. Penalized Discriminant Analysis

44. Adaptive Margin Support Vector Machines

45. Fast training of support vector machines using sequential minimal optimization, advances in kernel methods

46. Support vector regression with ANOVA decomposition kernels

47. Nonlinear prediction of chaotic time series using support vector machines

48. Efficient Pattern Recognition Using a New Transformation Distance

49. A Brief Introduction to Boosting

50. Support vector density estimation

51. Large Scale Bayes Point Machines

52. Learning algorithms for classification - A comparison on handwritten digit recognition

53. Regularized Discriminant Analysis

54. Estimating the Support of a High-Dimensional Distribution

55. Pattern classification and scene analysis

56. Bounds on Error Expectation for Support Vector Machines

57. Inductive learning algorithms and representations for text categorization

58. Transformation Invariance in Pattern Recognition-Tangent Distance and Tangent Propagation

59. Comparison of classifier methods - a case study in handwritten digit recognition

60. An improved training algorithm for support vector machines

61. Trends & Controversies - Support Vector Machines

62. Regularization Algorithms for Learning That Are Equivalent to Multilayer Networks

63. Kernel PCA Feature Extraction of Event-Related Potentials for Human Signal Detection Performance

64. Dynamic Alignment Kernels

65. Generalization performance of Bayes optimal classification algorithm for learning a perceptron.

66. Asymptotic Analysis of Penalized Likelihood and Related Estimators

67. Boosting Performance in Neural Networks

68. Extracting Support Data for a Given Task

69. From regularization to radial, tensor and additive splines

70. Neural Networks - Tricks of the Trade

71. A Discriminative Framework for Detecting Remote Protein Homologies

72. Design and analysis of efficient learning algorithms

73. Bayesian Learning in Reproducing Kernel Hilbert Spaces

74. Lagrangian Support Vector Machines

75. Prior Knowledge in Support Vector Kernels

76. A decision-theoretic generalization of on-line learning and an application to boosting

77. Improvements to Platt's SMO Algorithm for SVM Classifier Design

78. Principal Component Neural Networks - Theory and Applications

79. Predicting Time Series with Support Vector Machines

80. Arcing the edge

81. A geometric approach to leveraging weak learners

82. Statistical learning theory

83. Bayes Point Machines - Estimating the Bayes Point in Kernel Space

84. A Column Generation Algorithm For Boosting

85. Engineering Support Vector Machine Kerneis That Recognize Translation Initialion Sites

86. Support vector machine classification and validation of cancer tissue samples using microarray expression data

87. Mathematical Programming for Data Mining - Formulations and Challenges

88. Backpropagation Applied to Handwritten Zip Code Recognition

89. Small Sample Statistics for Classification Error Rates I - Error Rate Measurements

90. An Introduction to the Bootstrap

91. Comparison of learning algorithms for handwritten digit recognition

92. Neural Networks and the Bias/Variance Dilemma

93. Fast Learning in Networks of Locally-Tuned Processing Units

94. Potential Boosters?

95. Learning with kernels

96. The AdaTron - An Adaptive Perceptron Algorithm

97. Barrier Boosting

98. Robust Ensemble Learning

99. On optimal neural network learning

100. Optimal Learning with a Neural Network

101. Robust linear programming discrimination of two linearly inseparable sets

102. The Effective Number of Parameters - An Analysis of Generalization and Regularization in Nonlinear Learning Systems

103. Boosting the margin - A new explanation for the effectiveness of voting methods

104. Comparison of View-Based Object Recognition Algorithms Using Realistic 3D Models

105. Functional Gradient Techniques for Combining Hypotheses

106. Neural Networks - A Comprehensive Foundation

107. The Nature of Statistical Learning Theory

108. A PAC analysis of a Bayesian estimator

109. Arbitrary-norm separating plane

110. Improvements on Cross-Validation - The 632+ Bootstrap Method

111. Network information criterion-determining the number of hidden units for an artificial neural network model

112. Some results on Tchebycheffian spline functions

113. View-based 3D object recognition with support vector machines

114. Functions of Positive and Negative Type, and their Connection with the Theory of Integral Equations

115. Cryptographic limitations on learning Boolean formulae and finite automata

116. A new look at the statistical model identification

117. Playing Billiards in Version Space

118. Playing Billiard in Version Space

119. A framework for structural risk minimisation

120. Commentary - Interior-Point Methods - Algorithms and Formulations

121. A Multi-View Nonlinear Active Shape Model Using Kernel PCA

122. Prediction Games and Arcing Algorithms

123. A theory of the learnable

124. Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning

125. C

126. ESTScan - A Program for Detecting, Evaluating, and Reconstructing Potential Coding Regions in EST Sequences

127. Comparison of DNA sequences with protein sequences.

128. A method for identifying splice sites and translational start sites in eukaryotic mRNA

129. Lernen mit Kernen

130. Neural Network Prediction of Translation Initiation Sites in Eukaryotes - Perspectives for EST and Genome Analysis

131. N

132. G

133. Learning with kernels

134. Pattern Classification - A Unified View of Statistical and Neural Approaches.

135. Solutions of ill-posed problems

136. Theory of Reproducing Kernels and Its Applications

137. Nonlinear Programming

138. Advances in Kernel Methods - Support Vector Learning

139. Introduction to statistical pattern recognition (2nd ed.)

140. Improving the accuracy and speed of support vector learning machines

141. Support vector machines for dynamic reconstruction of a chaotic system

142. THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS

143. Integrating the evidence framework and the support vector machine

144. A Tutorial on Support Vector Machines for Pattern Recognition

145. Sparse Greedy Matrix Approximation for Machine Learning