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PAMI
2011
13 years 2 months ago
Semi-Supervised Learning via Regularized Boosting Working on Multiple Semi-Supervised Assumptions
—Semi-supervised learning concerns the problem of learning in the presence of labeled and unlabeled data. Several boosting algorithms have been extended to semi-supervised learni...
Ke Chen, Shihai Wang
CDC
2009
IEEE
139views Control Systems» more  CDC 2009»
14 years 5 days ago
Stabilization of networked control systems via non-monotone control ^lyapunov functions
— This paper deals with stabilization of networked control systems (NCS) affected by uncertain time-varying delays and data packet dropouts. We point out that such network effect...
Rob H. Gielen, Mircea Lazar
CDC
2008
IEEE
145views Control Systems» more  CDC 2008»
13 years 7 months ago
Necessary and sufficient conditions for success of the nuclear norm heuristic for rank minimization
Minimizing the rank of a matrix subject to constraints is a challenging problem that arises in many applications in control theory, machine learning, and discrete geometry. This c...
Benjamin Recht, Weiyu Xu, Babak Hassibi
ECAI
2004
Springer
14 years 25 days ago
On-Line Search for Solving Markov Decision Processes via Heuristic Sampling
In the past, Markov Decision Processes (MDPs) have become a standard for solving problems of sequential decision under uncertainty. The usual request in this framework is the compu...
Laurent Péret, Frédérick Garc...
SIAMJO
2000
108views more  SIAMJO 2000»
13 years 7 months ago
Smooth SQP Methods for Mathematical Programs with Nonlinear Complementarity Constraints
Mathematical programs with nonlinear complementarity constraints are reformulated using better-posed but nonsmooth constraints. We introduce a class of functions, parameterized by...
Houyuan Jiang, Daniel Ralph