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Semi-Supervised Learning
edited_book
Editor(s):
Olivier Chapelle
,
Bernhard Scholkopf
,
Alexander Zien
Publication date:
September 22 2006
Publisher:
The MIT Press
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Multimodal Learning Material
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Book
ISBN:
9780262033589
Publication date:
September 22 2006
DOI:
10.7551/mitpress/9780262033589.001.0001
SO-VID:
0f529347-c786-492c-8e6d-028622466707
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Book chapters
pp. 1
Introduction to Semi-Supervised Learning
pp. 14
A Taxonomy for Semi-Supervised Learning Methods
pp. 32
Semi-Supervised Text Classification Using EM
pp. 56
Risks of Semi-Supervised Learning: How Unlabeled Data Can Degrade Performance of Generative Classifiers
pp. 73
Probabilistic Semi-Supervised Clustering with Constraints
pp. 104
Transductive Support Vector Machines
pp. 118
Semi-Supervised Learning Using Semi-Definite Programming
pp. 136
Gaussian Processes and the Null-Category Noise Model
pp. 151
Entropy Regularization
pp. 169
Data-Dependent Regularization
pp. 192
Label Propagation and Quadratic Criterion
pp. 217
The Geometric Basis of Semi-Supervised Learning
pp. 236
Discrete Regularization
pp. 250
Semi-Supervised Learning with Conditional Harmonic Mixing
pp. 276
Graph Kernels by Spectral Transforms
pp. 292
Spectral Methods for Dimensionality Reduction
pp. 309
Modifying Distances
pp. 332
Large-Scale Algorithms
pp. 342
Semi-Supervised Protein Classification Using Cluster Kernels
pp. 361
Prediction of Protein Function from Networks
pp. 376
Analysis of Benchmarks
pp. 396
An Augmented PAC Model for Semi-Supervised Learning
pp. 420
Metric-Based Approaches for Semi-Supervised Regression and Classification
pp. 452
Transductive Inference and Semi-Supervised Learning
pp. 473
A Discussion of Semi-Supervised Learning and Transduction
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