Papers
4,122 papers found
Gaussian Processes for Ordinal Regression
Wei Chu, Zoubin Ghahramani
Generalization Bounds for the Area Under the ROC Curve
Shivani Agarwal, Thore Graepel, Ralf Herbrich et al.
Information Bottleneck for Gaussian Variables
Gal Chechik, Amir Globerson, Naftali Tishby et al.
Inner Product Spaces for Bayesian Networks
Atsuyoshi Nakamura, Michael Schmitt, Niels Schmitt et al.
Kernel Methods for Measuring Independence
Arthur Gretton, Ralf Herbrich, Alexander Smola et al.
Large Margin Methods for Structured and Interdependent Output Variables
Ioannis Tsochantaridis, Thorsten Joachims, Thomas Hofmann et al.
Learning a Mahalanobis Metric from Equivalence Constraints
Aharon Bar-Hillel, Tomer Hertz, Noam Shental et al.
Learning from Examples as an Inverse Problem
Ernesto De Vito, Lorenzo Rosasco, Andrea Caponnetto et al.
Learning Hidden Variable Networks: The Information Bottleneck Approach
Gal Elidan, Nir Friedman
Learning Module Networks
Eran Segal, Dana Pe'er, Aviv Regev et al.
Learning Multiple Tasks with Kernel Methods
Theodoros Evgeniou, Charles A. Micchelli, Massimiliano Pontil
Learning the Kernel Function via Regularization
Charles A. Micchelli, Massimiliano Pontil
Learning the Kernel with Hyperkernels
Cheng Soon Ong, Alexander J. Smola, Robert C. Williamson
Learning with Decision Lists of Data-Dependent Features
Mario Marchand, Marina Sokolova
Local Propagation in Conditional Gaussian Bayesian Networks
Robert G. Cowell
Loopy Belief Propagation: Convergence and Effects of Message Errors
Alexander T. Ihler, John W. Fisher III, Alan S. Willsky
Machine Learning Methods for Predicting Failures in Hard Drives: A Multiple-Instance Application
Joseph F. Murray, Gordon F. Hughes, Kenneth Kreutz-Delgado
Managing Diversity in Regression Ensembles
Gavin Brown, Jeremy L. Wyatt, Peter Tiň
Matrix Exponentiated Gradient Updates for On-line Learning and Bregman Projection
Koji Tsuda, Gunnar Rätsch, Manfred K. Warmuth
Maximum Margin Algorithms with Boolean Kernels
Roni Khardon, Rocco A. Servedio
Multiclass Boosting for Weak Classifiers
Günther Eibl, Karl-Peter Pfeiffer
Multiclass Classification with Multi-Prototype Support Vector Machines
Fabio Aiolli, Alessandro Sperduti
New Horn Revision Algorithms
Judy Goldsmith, Robert H. Sloan
On the Nystrom Method for Approximating a Gram Matrix for Improved Kernel-Based Learning
Petros Drineas, Michael W. Mahoney