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AAAI
2000
13 years 9 months ago
Restricted Bayes Optimal Classifiers
We introduce the notion of restricted Bayes optimal classifiers. These classifiers attempt to combine the flexibility of the generative approach to classification with the high ac...
Simon Tong, Daphne Koller
JMLR
2010
91views more  JMLR 2010»
13 years 2 months ago
Composite Binary Losses
We study losses for binary classification and class probability estimation and extend the understanding of them from margin losses to general composite losses which are the compos...
Mark D. Reid, Robert C. Williamson
JMLR
2008
111views more  JMLR 2008»
13 years 7 months ago
Ranking Categorical Features Using Generalization Properties
Feature ranking is a fundamental machine learning task with various applications, including feature selection and decision tree learning. We describe and analyze a new feature ran...
Sivan Sabato, Shai Shalev-Shwartz
ML
2002
ACM
220views Machine Learning» more  ML 2002»
13 years 7 months ago
Bayesian Methods for Support Vector Machines: Evidence and Predictive Class Probabilities
I describe a framework for interpreting Support Vector Machines (SVMs) as maximum a posteriori (MAP) solutions to inference problems with Gaussian Process priors. This probabilisti...
Peter Sollich
CSB
2005
IEEE
165views Bioinformatics» more  CSB 2005»
13 years 9 months ago
Sequential Diagonal Linear Discriminant Analysis (SeqDLDA) for Microarray Classification and Gene Identification
In microarray classification we are faced with a very large number of features and very few training samples. This is a challenge for classical Linear Discriminant Analysis (LDA),...
Roger Pique-Regi, Antonio Ortega, Shahab Asgharzad...