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CVPR
2009
IEEE
15 years 3 months ago
Unsupervised Maximum Margin Feature Selection with Manifold Regularization
Feature selection plays a fundamental role in many pattern recognition problems. However, most efforts have been focused on the supervised scenario, while unsupervised feature s...
Bin Zhao, James Tin-Yau Kwok, Fei Wang, Changshui ...
NIPS
2007
13 years 10 months ago
Regularized Boost for Semi-Supervised Learning
Semi-supervised inductive learning concerns how to learn a decision rule from a data set containing both labeled and unlabeled data. Several boosting algorithms have been extended...
Ke Chen 0001, Shihai Wang
ARSCOM
2004
124views more  ARSCOM 2004»
13 years 8 months ago
The Domatic Number of Regular Graphs
The domatic number of a graph G is the maximum number of dominating sets into which the vertex set of G can be partitioned. We show that the domatic number of a random r-regular g...
Peter Dankelmann, Neil J. Calkin
CORR
2008
Springer
107views Education» more  CORR 2008»
13 years 9 months ago
Randomized Methods for Linear Constraints: Convergence Rates and Conditioning
We study randomized variants of two classical algorithms: coordinate descent for systems of linear equations and iterated projections for systems of linear inequalities. Expanding...
D. Leventhal, A. S. Lewis
ENTCS
2008
106views more  ENTCS 2008»
13 years 8 months ago
Towards a Sharing Strategy for the Graph Rewriting Calculus
The graph rewriting calculus is an extension of the -calculus, handling graph like structures rather than simple terms. The calculus over terms is naturally generalized by using u...
Paolo Baldan, Clara Bertolissi, Horatiu Cirstea, C...