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» A Bayesian Metric for Evaluating Machine Learning Algorithms
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CICLING
2009
Springer
14 years 8 months ago
Semantic Clustering for a Functional Text Classification Task
Abstract. We describe a semantic clustering method designed to address shortcomings in the common bag-of-words document representation for functional semantic classification tasks....
Thomas Lippincott, Rebecca J. Passonneau
ICML
2009
IEEE
14 years 8 months ago
Geometry-aware metric learning
In this paper, we introduce a generic framework for semi-supervised kernel learning. Given pairwise (dis-)similarity constraints, we learn a kernel matrix over the data that respe...
Zhengdong Lu, Prateek Jain, Inderjit S. Dhillon
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
SDM
2009
SIAM
154views Data Mining» more  SDM 2009»
14 years 4 months ago
AMORI: A Metric-Based One Rule Inducer.
The requirements of real-world data mining problems vary extensively. It is plausible to assume that some of these requirements can be expressed as application-specific performan...
Niklas Lavesson, Paul Davidsson
ICML
2001
IEEE
14 years 8 months ago
Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers
Accurate, well-calibrated estimates of class membership probabilities are needed in many supervised learning applications, in particular when a cost-sensitive decision must be mad...
Bianca Zadrozny, Charles Elkan