Semi-Supervised Learning

1 Introduction to Semi-Supervised Learning

I Generative Models

2 A Taxonomy for Semi-Supervised Learning Methods

3 Semi-Supervised Text Classification Using EM

4 Risks of Semi-Supervised Learning

5 Probabilistic Semi-Supervised Clustering with Constraints

II Low-Density Separation

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

III Graph-Based Methods

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

IV Change of Representation

15 Graph Kernels by Spectral Transforms

16 Spectral Methods for Dimensionality

17 Modifying Distances

V Semi-Supervised Learning in Practice

18 Large-Scale Algorithms

19 Semi-Supervised Protein Classification Using Cluster Kernels

20 Prediction of Protein Function from Networks

21 Analysis of Benchmarks

VI Perspectives

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

About the Authors

Olivier Chapelle is Senior Research Scientist in Machine Learning at Yahoo.

Bernhard Schölkopf is Director at the Max Planck Institute for Intelligent Systems in Tübingen, Germany. He is coauthor of Learning with Kernels (2002) and is a coeditor of Advances in Kernel Methods: Support Vector Learning (1998), Advances in Large-Margin Classifiers (2000), and Kernel Methods in Computational Biology (2004), all published by the MIT Press.

Alexander Zien is Senior Analyst in Bioinformatics at LIFE Biosystems GmbH, Heidelberg.