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» Maximum entropy methods for biological sequence modeling
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ACL
1998
13 years 10 months ago
General-to-Specific Model Selection for Subcategorization Preference
This paper proposes a novel method for learning probability models of subcategorization preference of verbs. We consider the issues of case dependencies and noun class generalizat...
Takehito Utsuro, Takashi Miyata, Yuji Matsumoto
ICML
2009
IEEE
14 years 10 months ago
MedLDA: maximum margin supervised topic models for regression and classification
Supervised topic models utilize document's side information for discovering predictive low dimensional representations of documents; and existing models apply likelihoodbased...
Jun Zhu, Amr Ahmed, Eric P. Xing
BMCBI
2005
158views more  BMCBI 2005»
13 years 9 months ago
Automated generation of heuristics for biological sequence comparison
Background: Exhaustive methods of sequence alignment are accurate but slow, whereas heuristic approaches run quickly, but their complexity makes them more difficult to implement. ...
Guy St. C. Slater, Ewan Birney
ISMB
1996
13 years 10 months ago
Parameterization Studies for the SAM and HMMER Methods of Hidden Markov Model Generation
Multiple sequence alignment of distantly related viral proteins remains a challenge to all currently available alignment methods. The hidden Markovmodel approach offers a new,flex...
Marcella A. McClure, Chris Smith, Pete Elton
JMLR
2010
211views more  JMLR 2010»
13 years 3 months ago
Minimum Conditional Entropy Clustering: A Discriminative Framework for Clustering
In this paper, we introduce an assumption which makes it possible to extend the learning ability of discriminative model to unsupervised setting. We propose an informationtheoreti...
Bo Dai, Baogang Hu