- Title Pages
- Series Foreword
- Preface
-
1 Introduction to Semi-Supervised Learning -
1 A Taxonomy for Semi-Supervised Learning Methods -
3 Semi-Supervised Text Classification Using EM -
4 Risks of Semi-Supervised Learning: How Unlabeled Data Can Degrade Performance of Generative Classifiers -
5 Probabilistic Semi-Supervised Clustering with Constraints -
6 Transductive Support Vector Machines -
7 Semi-Supervised Learning Using Semi-Definite Programming -
8 Gaussian Processes and the Null-Category Noise Model -
9 Entropy Regularization -
10 Data-Dependent Regularization -
11 Label Propagation and Quadratic Criterion -
12 The Geometric Basis of Semi-Supervised Learning -
13 Discrete Regularization -
14 Semi-Supervised Learning with Conditional Harmonic Mixing -
15 Graph Kernels by Spectral Transforms -
16 Spectral Methods for Dimensionality Reduction -
17 Modifying Distances -
18 Large-Scale Algorithms -
19 Semi-Supervised Protein Classification Using Cluster Kernels -
20 Prediction of Protein Function from Networks -
25 Analysis of Benchmarks -
22 An Augmented PAC Model for Semi-Supervised Learning -
23 Metric-Based Approaches for Semi-Supervised Regression and Classification -
24 Transductive Inference and Semi-Supervised Learning -
25 A Discussion of Semi-Supervised Learning and Transduction - References
- Notation and Symbols
- Contributors
- Index
Data-Dependent Regularization
Data-Dependent Regularization
- Chapter:
- (p.169) 10 Data-Dependent Regularization
- Source:
- Semi-Supervised 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 semi-supervised 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 Semi-Supervised Learning -
1 A Taxonomy for Semi-Supervised Learning Methods -
3 Semi-Supervised Text Classification Using EM -
4 Risks of Semi-Supervised Learning: How Unlabeled Data Can Degrade Performance of Generative Classifiers -
5 Probabilistic Semi-Supervised Clustering with Constraints -
6 Transductive Support Vector Machines -
7 Semi-Supervised Learning Using Semi-Definite Programming -
8 Gaussian Processes and the Null-Category Noise Model -
9 Entropy Regularization -
10 Data-Dependent Regularization -
11 Label Propagation and Quadratic Criterion -
12 The Geometric Basis of Semi-Supervised Learning -
13 Discrete Regularization -
14 Semi-Supervised Learning with Conditional Harmonic Mixing -
15 Graph Kernels by Spectral Transforms -
16 Spectral Methods for Dimensionality Reduction -
17 Modifying Distances -
18 Large-Scale Algorithms -
19 Semi-Supervised Protein Classification Using Cluster Kernels -
20 Prediction of Protein Function from Networks -
25 Analysis of Benchmarks -
22 An Augmented PAC Model for Semi-Supervised Learning -
23 Metric-Based Approaches for Semi-Supervised Regression and Classification -
24 Transductive Inference and Semi-Supervised Learning -
25 A Discussion of Semi-Supervised Learning and Transduction - References
- Notation and Symbols
- Contributors
- Index
