Semi-supervised learning / [edited by] Olivier Chapelle, Bernhard Schölkopf, Alexander Zien.

Includes bibliographical references (pages 479-497).

Print version record.

A comprehensive review of an area of machine learning that deals with the use of unlabeled data in classification problems, this text looks at state-of-the-art algorithms, applications benchmark experiments, and directions for future research.

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 Criterion12 -- 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 Classification24 -- Transductive Inference and Semi-Supervised Learning; 25 -- A Discussion of Semi-Supervised Learning and Transduction; References; Notation and Symbols; Contributors; Index.

English.

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