- 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.

In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research.Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction.Olivier Chapelle and Alexander Zien are Research Scientists and Bernhard Scholkopf is Professor and Director at the Max Planck Institute for Biological Cybernetics in Tubingen. Scholkopf is coauthor of Learning with Kernels (MIT Press, 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.

(source: Nielsen Book Data)
A comprehensive review of an area of machine learning that deals with the use of unlabeled data in classification problems: state-of-the-art algorithms, a taxonomy of the field, applications, benchmark experiments, and directions for future research. In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research.Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction.

(source: Nielsen Book Data)