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TKDE
2010
168views more  TKDE 2010»
14 years 9 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...
CG
2000
Springer
15 years 3 months ago
Chess Neighborhoods, Function Combination, and Reinforcement Learning
Abstract. Over the years, various research projects have attempted to develop a chess program that learns to play well given little prior knowledge beyond the rules of the game. Ea...
Robert Levinson, Ryan Weber
ALMOB
2006
155views more  ALMOB 2006»
14 years 11 months ago
Refining motifs by improving information content scores using neighborhood profile search
The main goal of the motif finding problem is to detect novel, over-represented unknown signals in a set of sequences (e.g. transcription factor binding sites in a genome). The mo...
Chandan K. Reddy, Yao-Chung Weng, Hsiao-Dong Chian...
ICIP
2010
IEEE
14 years 9 months ago
A game theoretical approach for image denoising
How to adaptively choose optimal neighborhoods is very important to pixel-domain image denoising algorithms since too many neighborhoods may cause over-smooth artifacts and too fe...
Yan Chen, K. J. Ray Liu
SCALESPACE
2007
Springer
15 years 5 months ago
Bayesian Non-local Means Filter, Image Redundancy and Adaptive Dictionaries for Noise Removal
Abstract. Partial Differential equations (PDE), wavelets-based methods and neighborhood filters were proposed as locally adaptive machines for noise removal. Recently, Buades, Col...
Charles Kervrann, Jérôme Boulanger, P...