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» Sampling Methods for Unsupervised Learning
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145
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NIPS
2001
15 years 3 months ago
Natural Language Grammar Induction Using a Constituent-Context Model
This paper presents a novel approach to the unsupervised learning of syntactic analyses of natural language text. Most previous work has focused on maximizing likelihood according...
Dan Klein, Christopher D. Manning
CVPR
2009
IEEE
16 years 9 months ago
Active Learning for Large Multi-class Problems
Scarcity and infeasibility of human supervision for large scale multi-class classification problems necessitates active learning. Unfortunately, existing active learning methods ...
Prateek Jain (University of Texas at Austin), Ashi...
NN
1998
Springer
177views Neural Networks» more  NN 1998»
15 years 2 months ago
Soft vector quantization and the EM algorithm
The relation between hard c-means (HCM), fuzzy c-means (FCM), fuzzy learning vector quantization (FLVQ), soft competition scheme (SCS) of Yair et al. (1992) and probabilistic Gaus...
Ethem Alpaydin
112
Voted
ECML
2007
Springer
15 years 8 months ago
Dual Strategy Active Learning
Abstract. Active Learning methods rely on static strategies for sampling unlabeled point(s). These strategies range from uncertainty sampling and density estimation to multi-factor...
Pinar Donmez, Jaime G. Carbonell, Paul N. Bennett
105
Voted
CHI
2008
ACM
16 years 2 months ago
Experience sampling for building predictive user models: a comparative study
Experience sampling has been employed for decades to collect assessments of subjects' intentions, needs, and affective states. In recent years, investigators have employed au...
Ashish Kapoor, Eric Horvitz