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ICML
2009
IEEE
16 years 6 months ago
A stochastic memoizer for sequence data
We propose an unbounded-depth, hierarchical, Bayesian nonparametric model for discrete sequence data. This model can be estimated from a single training sequence, yet shares stati...
Frank Wood, Cédric Archambeau, Jan Gasthaus...
JIFS
2002
134views more  JIFS 2002»
15 years 5 months ago
Classification of gene expression data using fuzzy logic
Microarray technologies have allowed the measurement of expression of multiple genes simultaneously. Gene expression levels can be used to classify tissues into diagnostic or progn...
Lucila Ohno-Machado, Staal A. Vinterbo, Griffin We...
ICML
2004
IEEE
16 years 6 months ago
Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data
In sequence modeling, we often wish to represent complex interaction between labels, such as when performing multiple, cascaded labeling tasks on the same sequence, or when longra...
Charles A. Sutton, Khashayar Rohanimanesh, Andrew ...
IAT
2008
IEEE
16 years 2 days ago
Finding Minimum Data Requirements Using Pseudo-independence
In situations where Bayesian networks (BN) inferencing approximation is allowable, we show how to reduce the amount of sensory observations necessary and in a multi-agent context ...
Yoonheui Kim, Victor R. Lesser
NIPS
2008
15 years 7 months ago
Hierarchical Semi-Markov Conditional Random Fields for Recursive Sequential Data
Inspired by the hierarchical hidden Markov models (HHMM), we present the hierarchical semi-Markov conditional random field (HSCRF), a generalisation of embedded undirected Markov ...
Tran The Truyen, Dinh Q. Phung, Hung Hai Bui, Svet...