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ORCIDSemi-Supervised Learning 2006
- Olivier Chapelle, Bernhard Schölkopf, Alexander Zien:
Semi-Supervised Learning. The MIT Press 2006, ISBN 9780262033589 - Olivier Chapelle, Bernhard Schölkopf, Alexander Zien:
Introduction to Semi-Supervised Learning. 1-12 - Matthias W. Seeger:
A Taxonomy for Semi-Supervised Learning Methods. 14-31 - Kamal Nigam, Andrew McCallum, Tom M. Mitchell:
Semi-Supervised Text Classification Using EM. 32-55 - Fábio Gagliardi Cozman, Ira Cohen:
Risks of Semi-Supervised Learning: How Unlabeled Data Can Degrade Performance of Generative Classifiers. 56-72 - Sugato Basu, Mikhail Bilenko, Arindam Banerjee, Raymond J. Mooney:
Probabilistic Semi-Supervised Clustering with Constraints. 73-102 - Thorsten Joachims:
Transductive Support Vector Machines. 104-117 - Tijl De Bie, Nello Cristianini:
Semi-Supervised Learning Using Semi-Definite Programming. 118-135 - Neil D. Lawrence
, Michael I. Jordan:
Gaussian Processes and the Null-Category Noise Model. 136-150 - Yves Grandvalet, Yoshua Bengio:
Entropy Regularization. 151-168 - Adrian Corduneanu, Tommi S. Jaakkola:
Data-Dependent Regularization. 169-190 - Yoshua Bengio, Olivier Delalleau, Nicolas Le Roux:
Label Propagation and Quadratic Criterion. 192-216 - Vikas Sindhwani, Misha Belkin, Partha Niyogi:
The Geometric Basis of Semi-Supervised Learning. 217-235 - Dengyong Zhou, Bernhard Schölkopf:
Discrete Regularization. 236-249 - Christopher J. C. Burges, John C. Platt:
Semi-Supervised Learning with Conditional Harmonic Mixing. 250-273 - Xiaojin Zhu, Jaz S. Kandola, John Lafferty, Zoubin Ghahramani:
Graph Kernels by Spectral Transforms. 276-291 - Lawrence K. Saul, Kilian Q. Weinberger, Fei Sha, Jihun Ham, Daniel D. Lee:
Spectral Methods for Dimensionality Reduction. 292-308 - Alon Orlitsky:
Modifying Distances. 309-330 - Olivier Delalleau, Yoshua Bengio, Nicolas Le Roux:
Large-Scale Algorithms. 332-341 - Jason Weston, Christina S. Leslie, Eugene Ie, William Stafford Noble:
Semi-Supervised Protein Classification Using Cluster Kernels. 342-360 - Hyunjung Shin, Koji Tsuda:
Prediction of Protein Function from Networks. 361-375 - Olivier Chapelle, Bernhard Schölkopf, Alexander Zien:
Analysis of Benchmarks. 376-393 - Maria-Florina Balcan, Avrim Blum:
An Augmented PAC Model for Semi-Supervised Learning. 396-419 - Dale Schuurmans, Finnegan Southey, Dana F. Wilkinson, Yuhong Guo:
Metric-Based Approaches for Semi-Supervised Regression and Classification. 420-451 - Vladimir Vapnik:
Transductive Inference and Semi-Supervised Learning. 452-472 - Olivier Chapelle, Bernhard Schölkopf, Alexander Zien:
A Discussion of Semi-Supervised Learning and Transduction. 473-478
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