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NIPS
2008
13 years 9 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...
AIME
2007
Springer
14 years 1 months ago
Hierarchical Latent Class Models and Statistical Foundation for Traditional Chinese Medicine
Traditional Chinese medicine (TCM) is an important avenue for disease prevention and treatment for the Chinese people and is gaining popularity among others. However, many remain s...
Nevin Lianwen Zhang, Shihong Yuan, Tao Chen, Yi Wa...
NN
2008
Springer
201views Neural Networks» more  NN 2008»
13 years 7 months ago
Learning representations for object classification using multi-stage optimal component analysis
Learning data representations is a fundamental challenge in modeling neural processes and plays an important role in applications such as object recognition. In multi-stage Optima...
Yiming Wu, Xiuwen Liu, Washington Mio
ACL
2010
13 years 5 months ago
Global Learning of Focused Entailment Graphs
We propose a global algorithm for learning entailment relations between predicates. We define a graph structure over predicates that represents entailment relations as directed ed...
Jonathan Berant, Ido Dagan, Jacob Goldberger
CIKM
2009
Springer
14 years 2 months ago
Towards a universal wordnet by learning from combined evidence
Lexical databases are invaluable sources of knowledge about words and their meanings, with numerous applications in areas like NLP, IR, and AI. We propose a methodology for the au...
Gerard de Melo, Gerhard Weikum