Papers
4,122 papers found
An Empirical Study of the Use of Relevance Information in Inductive Logic Programming
Ashwin Srinivasan, Ross D. King, Michael E. Bain
A Neural Probabilistic Language Model
Yoshua Bengio, Réjean Ducharme, Pascal Vincent et al.
An Introduction to Variable and Feature Selection
Isabelle Guyon, André Elisseeff
A Unified Framework for Model-based Clustering
Shi Zhong, Joydeep Ghosh
Benefitting from the Variables that Variable Selection Discards
Rich Caruana, Virginia R. de Sa
Beyond Independent Components: Trees and Clusters
Francis R. Bach, Michael I. Jordan
Blind Separation of Post-nonlinear Mixtures using Linearizing Transformations and Temporal Decorrelation
Andreas Ziehe, Motoaki Kawanabe, Stefan Harmeling et al.
Blind Source Recovery: A Framework in the State Space
Khurram Waheed, Fathi M. Salem
Blind Source Separation via Generalized Eigenvalue Decomposition
Lucas Parra, Paul Sajda
Bottom-Up Relational Learning of Pattern Matching Rules for Information Extraction
Mary Elaine Califf, Raymond J. Mooney
Combining Knowledge from Different Sources in Causal Probabilistic Models
Marek J. Druzdzel, Francisco J. Díez
Concentration Inequalities for the Missing Mass and for Histogram Rule Error
David McAllester, Luis Ortiz
Dependence, Correlation and Gaussianity in Independent Component Analysis
Jean-François Cardoso
Designing Committees of Models through Deliberate Weighting of Data Points
Stefan W. Christensen, Ian Sinclair, Philippa A. S. Reed
Dimensionality Reduction via Sparse Support Vector Machines
Jinbo Bi, Kristin Bennett, Mark Embrechts et al.
Distributional Word Clusters vs. Words for Text Categorization
Ron Bekkerman, Ran El-Yaniv, Naftali Tishby et al.
Energy-Based Models for Sparse Overcomplete Representations
Yee Whye Teh, Max Welling, Simon Osindero et al.
Extensions to Metric-Based Model Selection
Yoshua Bengio, Nicolas Chapados
FINkNN: A Fuzzy Interval Number k-Nearest Neighbor Classifier for Prediction of Sugar Production from Populations of Samples
Vassilios Petridis, Vassilis G. Kaburlasos
Fusion of Domain Knowledge with Data for Structural Learning in Object Oriented Domains
Helge Langseth, Thomas D. Nielsen
Generalization Error Bounds for Bayesian Mixture Algorithms
Ron Meir, Tong Zhang
Grafting: Fast, Incremental Feature Selection by Gradient Descent in Function Space
Simon Perkins, Kevin Lacker, James Theiler