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ICML
2007
IEEE
16 years 1 months ago
Multi-task learning for sequential data via iHMMs and the nested Dirichlet process
A new hierarchical nonparametric Bayesian model is proposed for the problem of multitask learning (MTL) with sequential data. Sequential data are typically modeled with a hidden M...
Kai Ni, Lawrence Carin, David B. Dunson
121
Voted
SIGIR
2008
ACM
15 years 11 days ago
Enhancing text clustering by leveraging Wikipedia semantics
Most traditional text clustering methods are based on "bag of words" (BOW) representation based on frequency statistics in a set of documents. BOW, however, ignores the ...
Jian Hu, Lujun Fang, Yang Cao, Hua-Jun Zeng, Hua L...
95
Voted
BMCBI
2005
99views more  BMCBI 2005»
15 years 10 days ago
Super paramagnetic clustering of protein sequences
Background: Detection of sequence homologues represents a challenging task that is important for the discovery of protein families and the reliable application of automatic annota...
Igor V. Tetko, Axel Facius, Andreas Ruepp, Hans-We...
118
Voted
RECOMB
2005
Springer
16 years 23 days ago
Individual Gene Cluster Statistics in Noisy Maps
Abstract. Identification of homologous chromosomal regions is important for understanding evolutionary processes that shape genome evolution, such as genome rearrangements and larg...
Narayanan Raghupathy, Dannie Durand
129
Voted
DASFAA
2008
IEEE
190views Database» more  DASFAA 2008»
15 years 7 months ago
Analysis of Time Series Using Compact Model-Based Descriptions
Abstract. Recently, we have proposed a novel method for the compression of time series based on mathematical models that explore dependencies between different time series. This r...
Hans-Peter Kriegel, Peer Kröger, Alexey Pryak...