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ICML
2007
IEEE
14 years 9 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
MLDM
2009
Springer
14 years 3 months ago
The Needles-in-Haystack Problem
We consider a new data mining problem of detecting the members of a rare class of data, the needles, that have been hidden in a set of records, the haystack. Besides the haystack, ...
Katherine Moreland, Klaus Truemper
NIPS
1996
13 years 10 months ago
Continuous Sigmoidal Belief Networks Trained using Slice Sampling
Real-valued random hidden variables can be useful for modelling latent structure that explains correlations among observed variables. I propose a simple unit that adds zero-mean G...
Brendan J. Frey
ICML
2005
IEEE
14 years 9 months ago
Expectation maximization algorithms for conditional likelihoods
We introduce an expectation maximizationtype (EM) algorithm for maximum likelihood optimization of conditional densities. It is applicable to hidden variable models where the dist...
Jarkko Salojärvi, Kai Puolamäki, Samuel ...
EMNLP
2007
13 years 10 months ago
Bootstrapping Information Extraction from Field Books
We present two machine learning approaches to information extraction from semi-structured documents that can be used if no annotated training data are available, but there does ex...
Sander Canisius, Caroline Sporleder