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COLT
2008
Springer
13 years 11 months ago
Learning in the Limit with Adversarial Disturbances
We study distribution-dependent, data-dependent, learning in the limit with adversarial disturbance. We consider an optimization-based approach to learning binary classifiers from...
Constantine Caramanis, Shie Mannor
ML
1998
ACM
102views Machine Learning» more  ML 1998»
13 years 9 months ago
Statistical Mechanics of Online Learning of Drifting Concepts: A Variational Approach
We review the application of statistical mechanics methods to the study of online learning of a drifting concept in the limit of large systems. The model where a feed-forward netwo...
Renato Vicente, Osame Kinouchi, Nestor Caticha
ICIP
2008
IEEE
14 years 11 months ago
A supervised nonlinear neighborhood embedding of color histogram for image indexing
Subspace learning techniques are widespread in pattern recognition research. They include PCA, ICA, LPP, etc. These techniques are generally linear and unsupervised. The problem o...
Xian-Hua Han, Yen-Wei Chen, Takeshi Sukegawa
ICCV
2011
IEEE
12 years 9 months ago
Sparse Dictionary-based Representation and Recognition of Action Attributes
We present an approach for dictionary learning of action attributes via information maximization. We unify the class distribution and appearance information into an objective func...
Qiang Qiu, Zhuolin Jiang, Rama Chellappa
CORR
2006
Springer
101views Education» more  CORR 2006»
13 years 9 months ago
MDL Convergence Speed for Bernoulli Sequences
The Minimum Description Length principle for online sequence estimation/prediction in a proper learning setup is studied. If the underlying model class is discrete, then the total...
Jan Poland, Marcus Hutter