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SODA
2001
ACM
79views Algorithms» more  SODA 2001»
15 years 1 months ago
Learning Markov networks: maximum bounded tree-width graphs
Markov networks are a common class of graphical models used in machine learning. Such models use an undirected graph to capture dependency information among random variables in a ...
David R. Karger, Nathan Srebro
83
Voted
UIST
1994
ACM
15 years 4 months ago
Evolutionary Learning of Graph Layout Constraints from Examples
We propose a new evolutionary method of extracting user preferences from examples shown to an automatic graph layout system. Using stochastic methods such as simulated annealing a...
Toshiyuki Masui
94
Voted
EMNLP
2010
14 years 10 months ago
A Semi-Supervised Method to Learn and Construct Taxonomies Using the Web
Although many algorithms have been developed to harvest lexical resources, few organize the mined terms into taxonomies. We propose (1) a semi-supervised algorithm that uses a roo...
Zornitsa Kozareva, Eduard H. Hovy
119
Voted
NAACL
2010
14 years 10 months ago
Hitting the Right Paraphrases in Good Time
We present a random-walk-based approach to learning paraphrases from bilingual parallel corpora. The corpora are represented as a graph in which a node corresponds to a phrase, an...
Stanley Kok, Chris Brockett
115
Voted
NIPS
2008
15 years 1 months ago
Structure Learning in Human Sequential Decision-Making
We use graphical models and structure learning to explore how people learn policies in sequential decision making tasks. Studies of sequential decision-making in humans frequently...
Daniel Acuña, Paul R. Schrater