Description 
x, 508 pages : illustrations ; 26 cm 
Series 
Adaptive computation and machine learning 

Adaptive computation and machine learning.

Contents 
1. Introduction to semisupervised learning  2. A taxonomy for semisupervised learning methods / Matthias Seeger  3. Semisupervised text classification using EM / Kamal Nigam, Andrew McCallum and Tom Mitchell  4. Risks of semisupervised learning / Fabio Cozman and Ira Cohen  5. Probabilistic semisupervised clustering with constraints / Sugato Basu, Mikhail Bilenko, Arindam Benerjee and Raymond Mooney  6. Transductive support vector machines / Thorsten Joachims  7. Semisupervised learning using semidefinite programming / Tijl De Bie and Nello Cristianini  8. Gaussian processes and the nullcategory noise model / Neil D. Lawrence and Michael I. Jordan  9. Entropy regularization / Yves Grandvalet and Yoshua Bengio  10. Datadependent 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 semisupervised learning / Vikas Sindhwani, Misha Belkin and Partha Niyogi  13. Discrete regularization / Dengyong Zhou and Bernhard Scholkopf  14. Semisupervised 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. Largescale algorithms / Olivier Delalleau, Yoshua Bengio and Nicolas Le Roux  19. Semisupervised 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 semisupervised learning / MariaFlorina Balcan and Avrim Blum  23. Metricbased approaches for semisupervised regression and classification / Dale Schuuramans, Finnegan Southey, Dana Wilkinson and Yuhong Guo  24. Transductive inference and semisupervised learning / Vladimir Vapnik  25. A discussion of semisupervised learning and transduction 
Summary 
In the field of machine learning, semisupervised 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 stateoftheart algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research. SemiSupervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or lowdensity 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 lowdensity separation assumption, graphbased methods, and algorithms that perform twostep 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 semisupervised 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) 
