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PAMI
2011
14 years 9 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»
15 years 6 months 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»
15 years 2 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
15 years 7 months 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»
15 years 1 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