 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
DataDependent Regularization
DataDependent Regularization
 Chapter:
 (p.169) 10 DataDependent Regularization
 Source:
 SemiSupervised Learning
 Author(s):
Corduneanu Adrian
Jaakkola Tommi
 Publisher:
 The MIT Press
This chapter considers two ways of representing the topology over examples, either based on complete knowledge of the marginal density or by grouping together examples whose labels should be related. The learning algorithms and sample complexity issues that result from each representation is discussed here. Information regularization is a principle for assigning labels to unlabeled data points in a semisupervised setting. The broader principle is based on finding labels that minimize the information induced between examples and labels relative to a topology over the examples; any label variation within a small local region of examples ties together the identities of examples and their labels. Such variation should be minimized unless supported directly or indirectly by the available labeled examples.
Keywords: topology over examples, marginal density, learning algorithms, sample complexity issues, information regularization, unlabeled data points
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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