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TKDE
2010
168views more  TKDE 2010»
14 years 8 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
15 years 10 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
MVA
2010
181views Computer Vision» more  MVA 2010»
14 years 8 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...
ICML
2010
IEEE
14 years 10 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
ICIP
2005
IEEE
15 years 11 months ago
Local manifold matching for face recognition
In this paper, we propose a novel classification method, called local manifold matching (LMM), for face recognition. LMM has great representational capacity of available prototypes...
Wei Liu, Wei Fan, Yunhong Wang, Tieniu Tan