 Title Pages
 Series Foreword
 Preface

1 Introduction to SemiSupervised Learning 
1 A Taxonomy for SemiSupervised Learning Methods 
3 SemiSupervised Text Classification Using EM 
4 Risks of SemiSupervised Learning: How Unlabeled Data Can Degrade Performance of Generative Classifiers 
5 Probabilistic SemiSupervised Clustering with Constraints 
6 Transductive Support Vector Machines 
7 SemiSupervised Learning Using SemiDefinite Programming 
8 Gaussian Processes and the NullCategory Noise Model 
9 Entropy Regularization 
10 DataDependent Regularization 
11 Label Propagation and Quadratic Criterion 
12 The Geometric Basis of SemiSupervised Learning 
13 Discrete Regularization 
14 SemiSupervised Learning with Conditional Harmonic Mixing 
15 Graph Kernels by Spectral Transforms 
16 Spectral Methods for Dimensionality Reduction 
17 Modifying Distances 
18 LargeScale Algorithms 
19 SemiSupervised Protein Classification Using Cluster Kernels 
20 Prediction of Protein Function from Networks 
25 Analysis of Benchmarks 
22 An Augmented PAC Model for SemiSupervised Learning 
23 MetricBased Approaches for SemiSupervised Regression and Classification 
24 Transductive Inference and SemiSupervised Learning 
25 A Discussion of SemiSupervised Learning and Transduction  References
 Notation and Symbols
 Contributors
 Index
Transductive Support Vector Machines
Transductive Support Vector Machines
 Chapter:
 (p.104) (p.105) 6 Transductive Support Vector Machines
 Source:
 SemiSupervised Learning
 Author(s):
Joachims Thorsten
 Publisher:
 The MIT Press
This chapter discusses the transductive learning setting proposed by Vapnik where predictions are made only at a fixed number of known test points. Transductive support vector machines (TSVMs) implement the idea of transductive learning by including test points in the computation of the margin. This chapter provides some examples for why the margin on the test examples can provide useful prior information for learning, in particular for the problem of text classification. The resulting optimization problems, however, are difficult to solve. The chapter reviews exact and approximate optimization methods and discusses their properties. Finally, the chapter discusses connections to other related semisupervised learning approaches such as cotraining and methods based on graph cuts, which can be seen as solving variants of the TSVM optimization problem.
Keywords: transductive learning setting, Vapnik, transductive support vector machines, TSVMs, text classification, optimization problems, cotraining, graph cuts
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 Title Pages
 Series Foreword
 Preface

1 Introduction to SemiSupervised Learning 
1 A Taxonomy for SemiSupervised Learning Methods 
3 SemiSupervised Text Classification Using EM 
4 Risks of SemiSupervised Learning: How Unlabeled Data Can Degrade Performance of Generative Classifiers 
5 Probabilistic SemiSupervised Clustering with Constraints 
6 Transductive Support Vector Machines 
7 SemiSupervised Learning Using SemiDefinite Programming 
8 Gaussian Processes and the NullCategory Noise Model 
9 Entropy Regularization 
10 DataDependent Regularization 
11 Label Propagation and Quadratic Criterion 
12 The Geometric Basis of SemiSupervised Learning 
13 Discrete Regularization 
14 SemiSupervised Learning with Conditional Harmonic Mixing 
15 Graph Kernels by Spectral Transforms 
16 Spectral Methods for Dimensionality Reduction 
17 Modifying Distances 
18 LargeScale Algorithms 
19 SemiSupervised Protein Classification Using Cluster Kernels 
20 Prediction of Protein Function from Networks 
25 Analysis of Benchmarks 
22 An Augmented PAC Model for SemiSupervised Learning 
23 MetricBased Approaches for SemiSupervised Regression and Classification 
24 Transductive Inference and SemiSupervised Learning 
25 A Discussion of SemiSupervised Learning and Transduction  References
 Notation and Symbols
 Contributors
 Index