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Semi-supervised learning

Author: Olivier Chapelle; Bernhard Schölkopf; Alexander Zien
Publisher: Cambridge, Mass. ; London : MIT, 2010.
Series: Adaptive computation and machine learning.
Edition/Format:   Print book : English : 1st MIT Press paperback edView all editions and formats
Summary:
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  Read more...
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Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Olivier Chapelle; Bernhard Schölkopf; Alexander Zien
ISBN: 9780262514125 0262514125 9780262033589 0262033585
OCLC Number: 457159745
Description: x, 508 pages : illustrations ; 26 cm.
Contents: 1. Introduction to Semi-Supervised Learning ---
Part I. Generative Models. 2. A Taxonomy for Semi-Supervised Learning Methods / Matthias Seeger --
3. Semi-Supervised Text Classification Using EM / Kamal Nigam, Andrew McCallum, Tom Mitchell --
4. Risks of Semi-Supervised Learning / Fabio Cozman, Ira Cohen --
5. Probabilistic Semi-Supervised Clustering with Constraints / Sugato Basu, Mikhail Bilenko, Arindam Banerjee, Raymond Mooney ---
Part II. Low-Density Separation. 6. Transductive Support Vector Machines/ Thorsten Joachims --
7. Semi-Supervised Learning Using Semi-Definite Programming / Tijl De Bie, Nello Cristianini --
8. Gaussian Processes and the Null-Category Noise Model / Neil D. Lawrence, Michael I. Jordan --
9. Entropy Regularization / Yves Grandvalet, Yoshua Bengio --
10. Data-Dependent Regularization / Adrian Corduneanu, Tommi Jaakkola ---
Part III. Graph-Based Methods. 11. Label Propagation and Quadratic Criterion / Yoshua Bengio, Olivier Delalleau, Nicolas Le Roux --
12. The Geometric Basis of Semi-Supervised Learning / Vikas Sindhwani, Misha Belkin, Partha Niyogi --
13. Discrete Regularization / Dengyong Zhou, Bernhard Scholkopf --
14. Semi-Supervised Learning with Conditional Harmonic Mixing / Christopher J.C. Burges, John C. Platt ---
Part IV. Change of Representation. 15. Graph Kernels by Spectral Transforms / Xiaojin Zhu, Jaz Kandola, John Lafferty, Zoubin Ghahramani --
16. Spectral Methods for Dimensionality Reduction / awrence K. Saul, Kilian Q. Weinberger, Fei Sha, Jihun Ham, Daniel D. Lee --
17. Modifying Distances / Sajama, Alon Orlitsky ---
Part V. Semi-Supervised Learning in Practice. 18. Large-Scale Algorithms / Olivier Delalleau, Yoshua Bengio, Nicolas Le Roux --
19. Semi-Supervised Protein Classification Using Cluster Kernels / Jason Weston, Christina Leslie, Eugene Ie, William Stafford Noble --
20. Prediction of Protein Function from Networks / Hyunjung Shin, Koji Tsuda --
21. Analysis of Benchmarks ---
Part VI. Perspectives. 22. An Augmented PAC Model for Semi-Supervised Learning/ Maria-Florina Balcan, Avrim Blum --
23. Metric-Based Approaches for Semi-Supervised Regression and Classification / Dale Schuurmans, Finnegan Southey, Dana Wilkinson, Yuhong Guom --
24. Transductive Inference and Semi-Supervised Learning / Vladimir Vapnik --
25. A Discussion of Semi-Supervised Learning and Transduction.
Series Title: Adaptive computation and machine learning.
Responsibility: edited by Olivier Chapelle, Bernhard Schölkopf, and Alexander Zien.

Abstract:

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,  Read more...

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In summary, reading this book is a delightful journey through semi-supervised learning.-Hsun-Hsien Chang, Computing Reviews In summary, reading this book is a delightful journey through Read more...

 
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