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Title Semi-supervised learning / [edited by] Olivier Chapelle, Bernhard Schölkopf, Alexander Zien
Edition First MIT Press Paperback edition
Published Cambridge, Mass. ; London : MIT, 2006


Location Call no. Vol. Availability
 W'PONDS  006.31 Cha/Ssl  AVAILABLE
Description x, 508 pages : illustrations ; 26 cm
Series Adaptive computation and machine learning
Adaptive computation and machine learning.
Contents 1. Introduction to semi-supervised learning -- 2. A taxonomy for semi-supervised learning methods / Matthias Seeger -- 3. Semi-supervised text classification using EM / Kamal Nigam, Andrew McCallum and Tom Mitchell -- 4. Risks of semi-supervised learning / Fabio Cozman and Ira Cohen -- 5. Probabilistic semi-supervised clustering with constraints / Sugato Basu, Mikhail Bilenko, Arindam Benerjee and Raymond Mooney -- 6. Transductive support vector machines / Thorsten Joachims -- 7. Semi-supervised learning using semi-definite programming / Tijl De Bie and Nello Cristianini -- 8. Gaussian processes and the null-category noise model / Neil D. Lawrence and Michael I. Jordan -- 9. Entropy regularization / Yves Grandvalet and Yoshua Bengio -- 10. Data-dependent regularization / Adrian Corduneanu and Tommi Jaakkola -- 11. Label propagation and quadratic criterion / Yoshua Bengio, Olivier Delalleau and Nicolas Le Roux -- 12. The geometric basis of semi-supervised learning / Vikas Sindhwani, Misha Belkin and Partha Niyogi -- 13. Discrete regularization / Dengyong Zhou and Bernhard Scholkopf -- 14. Semi-supervised learning with conditional harmonic mixing / Christopher J. C. Burges and John C. Platt -- 15. Graph kernels by spectral transforms / Xiaojin Zhu, Jaz Kandola, John Lafferty and Zoubin Ghahramani -- 16. Spectral methods for dimensionality reduction / Lawrence K. Saul, Kilian Q. Weinberger, Fei Sha, Jihun Ham and Daniel D. Lee -- 17. Modifying distances / Sajama and Alon Orlitsky -- 18. Large-scale algorithms / Olivier Delalleau, Yoshua Bengio and Nicolas Le Roux -- 19. Semi-supervised protein classification using cluster kernels / Jason Westton, Christina Leslie, Eugene Ie and William Stafford Noble -- 20. Prediction of protein function from networks / Hyunjung Shin and Koji Tsuda -- 21. Analysis of benchmarks -- 22. An augmented PAC model for semi-supervised learning / Maria-Florina Balcan and Avrim Blum -- 23. Metric-based approaches for semi-supervised regression and classification / Dale Schuuramans, Finnegan Southey, Dana Wilkinson and Yuhong Guo -- 24. Transductive inference and semi-supervised learning / Vladimir Vapnik -- 25. A discussion of semi-supervised learning and transduction
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 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
Analysis COMPUTER SCIENCE/Machine Learning & Neural Networks
Notes Formerly CIP. Uk
Bibliography Includes bibliographical references (pages [479]-497)
Notes Print version record
Subject Machine learning.
Supervised learning (Machine learning)
Author Chapelle, Olivier.
Schölkopf, Bernhard.
Zien, Alexander.
LC no. 2006044448
ISBN 0262033585 (hbk.)
0262514125 (paperback)
9780262033589 (hbk.)
9780262514125 (paperback)