Contents -- Series Foreword -- Preface -- 1 - Introduction to Semi-Supervised Learning -- 2 - 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 -- 21 - 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.