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NIPS
1992
14 years 11 months ago
A Note on Learning Vector Quantization
Vector Quantization is useful for data compression. Competitive Learning which minimizes reconstruction error is an appropriate algorithm for vector quantization of unlabelled dat...
Virginia R. de Sa, Dana H. Ballard
CORR
2010
Springer
146views Education» more  CORR 2010»
14 years 10 months ago
Adaptive Submodularity: A New Approach to Active Learning and Stochastic Optimization
Solving stochastic optimization problems under partial observability, where one needs to adaptively make decisions with uncertain outcomes, is a fundamental but notoriously diffic...
Daniel Golovin, Andreas Krause
KI
2008
Springer
14 years 10 months ago
Learning by Observing: Case-Based Decision Making in Complex Strategy Games
Abstract. There is a growing research interest in the design of competitive and adaptive Game AI for complex computer strategy games. In this paper, we present a novel approach for...
Darko Obradovic, Armin Stahl
CVPR
2009
IEEE
16 years 5 months ago
A Revisit of Generative Model for Automatic Image Annotation using Markov Random Fields
Much research effort on Automatic Image Annotation (AIA) has been focused on Generative Model, due to its well formed theory and competitive performance as compared with many we...
Yu Xiang (Fudan University), Xiangdong Zhou (Fudan...
ICCV
2009
IEEE
1419views Computer Vision» more  ICCV 2009»
16 years 3 months ago
On Feature Combination for Multiclass Object Classification
A key ingredient in the design of visual object classification systems is the identification of relevant class specific aspects while being robust to intra-class variations. Whil...
Peter Gehler, Sebastian Nowozin