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JMLR
2010
121views more  JMLR 2010»
13 years 5 months ago
Sparse Semi-supervised Learning Using Conjugate Functions
In this paper, we propose a general framework for sparse semi-supervised learning, which concerns using a small portion of unlabeled data and a few labeled data to represent targe...
Shiliang Sun, John Shawe-Taylor
TSP
2010
13 years 4 months ago
Randomized and distributed self-configuration of wireless networks: two-layer Markov random fields and near-optimality
Abstract--This work studies the near-optimality versus the complexity of distributed configuration management for wireless networks. We first develop a global probabilistic graphic...
Sung-eok Jeon, Chuanyi Ji
COLT
2001
Springer
14 years 2 months ago
Smooth Boosting and Learning with Malicious Noise
We describe a new boosting algorithm which generates only smooth distributions which do not assign too much weight to any single example. We show that this new boosting algorithm ...
Rocco A. Servedio
COLT
2000
Springer
14 years 2 months ago
PAC Analogues of Perceptron and Winnow via Boosting the Margin
We describe a novel family of PAC model algorithms for learning linear threshold functions. The new algorithms work by boosting a simple weak learner and exhibit complexity bounds...
Rocco A. Servedio
INFOCOM
2007
IEEE
14 years 4 months ago
Randomized k-Coverage Algorithms For Dense Sensor Networks
— We propose new algorithms to achieve k-coverage in dense sensor networks. In such networks, covering sensor locations approximates covering the whole area. However, it has been...
Mohamed Hefeeda, M. Bagheri