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ICML
2005
IEEE
16 years 8 months ago
Learning the structure of Markov logic networks
Markov logic networks (MLNs) combine logic and probability by attaching weights to first-order clauses, and viewing these as templates for features of Markov networks. In this pap...
Stanley Kok, Pedro Domingos
ICMCS
2007
IEEE
214views Multimedia» more  ICMCS 2007»
16 years 1 months ago
Exploring Discriminative Learning for Text-Independent Speaker Recognition
Speaker verification is a technology of verifying the claimed identity of a speaker based on the speech signal from the speaker (voice print). To learn the score of similarity be...
Ming Liu, Zhengyou Zhang, Mark Hasegawa-Johnson, T...
UMUAI
2008
192views more  UMUAI 2008»
15 years 7 months ago
Automatic detection of learner's affect from conversational cues
We explored the reliability of detecting a learner's affect from conversational features extracted from interactions with AutoTutor, an intelligent tutoring system that helps...
Sidney K. D'Mello, Scotty D. Craig, Amy M. Withers...
AAAI
2006
15 years 8 months ago
Mixed Collaborative and Content-Based Filtering with User-Contributed Semantic Features
We describe a recommender system which uses a unique combination of content-based and collaborative methods to suggest items of interest to users, and also to learn and exploit it...
Matthew Garden, Gregory Dudek
ICML
2010
IEEE
15 years 8 months ago
Online Learning for Group Lasso
We develop a novel online learning algorithm for the group lasso in order to efficiently find the important explanatory factors in a grouped manner. Different from traditional bat...
Haiqin Yang, Zenglin Xu, Irwin King, Michael R. Ly...