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SETN
2004
Springer
16 years 29 days ago
Incremental Mixture Learning for Clustering Discrete Data
Abstract. This paper elaborates on an efficient approach for clustering discrete data by incrementally building multinomial mixture models through likelihood maximization using the...
Konstantinos Blekas, Aristidis Likas
WEBDB
2010
Springer
155views Database» more  WEBDB 2010»
16 years 20 days ago
Learning Topical Transition Probabilities in Click Through Data with Regression Models
The transition of search engine users’ intents has been studied for a long time. The knowledge of intent transition, once discovered, can yield a better understanding of how diï...
Xiao Zhang, Prasenjit Mitra
KR
1992
Springer
15 years 11 months ago
Learning Useful Horn Approximations
While the task of answering queries from an arbitrary propositional theory is intractable in general, it can typicallybe performed e ciently if the theory is Horn. This suggests t...
Russell Greiner, Dale Schuurmans
CDC
2009
IEEE
158views Control Systems» more  CDC 2009»
15 years 11 months ago
Multiple target detection using Bayesian learning
In this paper, we study multiple target detection using Bayesian learning. The main aim of the paper is to present a computationally efficient way to compute the belief map update ...
Sujit Nair, Konda Reddy Chevva, Houman Owhadi, Jer...
NIPS
2003
15 years 9 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