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PR
2010
156views more  PR 2010»
13 years 8 months ago
Semi-supervised clustering with metric learning: An adaptive kernel method
Most existing representative works in semi-supervised clustering do not sufficiently solve the violation problem of pairwise constraints. On the other hand, traditional kernel met...
Xuesong Yin, Songcan Chen, Enliang Hu, Daoqiang Zh...
LWA
2004
13 years 11 months ago
Modeling Rule Precision
This paper reports first results of an empirical study of the precision of classification rules on an independent test set. We generated a large number of rules using a general co...
Johannes Fürnkranz
ICML
2005
IEEE
14 years 11 months ago
Learning class-discriminative dynamic Bayesian networks
In many domains, a Bayesian network's topological structure is not known a priori and must be inferred from data. This requires a scoring function to measure how well a propo...
John Burge, Terran Lane
NIPS
2003
13 years 11 months ago
Learning the k in k-means
When clustering a dataset, the right number k of clusters to use is often not obvious, and choosing k automatically is a hard algorithmic problem. In this paper we present an impr...
Greg Hamerly, Charles Elkan
TNN
2008
119views more  TNN 2008»
13 years 10 months ago
Selecting Useful Groups of Features in a Connectionist Framework
Abstract--Suppose for a given classification or function approximation (FA) problem data are collected using sensors. From the output of the th sensor, features are extracted, ther...
Debrup Chakraborty, Nikhil R. Pal