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TKDE
2010
168views more  TKDE 2010»
15 years 2 months ago
Completely Lazy Learning
—Local classifiers are sometimes called lazy learners because they do not train a classifier until presented with a test sample. However, such methods are generally not complet...
Eric K. Garcia, Sergey Feldman, Maya R. Gupta, San...
ICML
2004
IEEE
16 years 4 months ago
A needle in a haystack: local one-class optimization
This paper addresses the problem of finding a small and coherent subset of points in a given data. This problem, sometimes referred to as one-class or set covering, requires to fi...
Koby Crammer, Gal Chechik
131
Voted
MVA
2010
181views Computer Vision» more  MVA 2010»
15 years 2 months ago
Human action detection via boosted local motion histograms
This paper presents a novel learning method for human action detection in video sequences. The detecting problem is not limited in controlled settings like stationary background or...
Qingshan Luo, Xiaodong Kong, Guihua Zeng, Jianping...
114
Voted
AIMSA
2004
Springer
15 years 9 months ago
The Web as an Autobiographical Agent
The reward-based autobiographical memory approach has been applied to the Web search agent. The approach is based on the analogy between the Web and the environmental exploration b...
Maya Dimitrova, Emilia I. Barakova, Tino Lourens, ...
148
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ICML
2010
IEEE
15 years 4 months ago
Non-Local Contrastive Objectives
Pseudo-likelihood and contrastive divergence are two well-known examples of contrastive methods. These algorithms trade off the probability of the correct label with the probabili...
David Vickrey, Cliff Chiung-Yu Lin, Daphne Koller