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ICASSP
2008
IEEE
14 years 1 months ago
Learning the kernel via convex optimization
The performance of a kernel-based learning algorithm depends very much on the choice of the kernel. Recently, much attention has been paid to the problem of learning the kernel it...
Seung-Jean Kim, Argyrios Zymnis, Alessandro Magnan...
ML
2002
ACM
121views Machine Learning» more  ML 2002»
13 years 6 months ago
Near-Optimal Reinforcement Learning in Polynomial Time
We present new algorithms for reinforcement learning, and prove that they have polynomial bounds on the resources required to achieve near-optimal return in general Markov decisio...
Michael J. Kearns, Satinder P. Singh
FOCS
2005
IEEE
14 years 10 days ago
Mechanism Design via Machine Learning
We use techniques from sample-complexity in machine learning to reduce problems of incentive-compatible mechanism design to standard algorithmic questions, for a wide variety of r...
Maria-Florina Balcan, Avrim Blum, Jason D. Hartlin...
LCN
2006
IEEE
14 years 22 days ago
Training on multiple sub-flows to optimise the use of Machine Learning classifiers in real-world IP networks
Literature on the use of machine learning (ML) algorithms for classifying IP traffic has relied on fullflows or the first few packets of flows. In contrast, many real-world scenar...
Thuy T. T. Nguyen, Grenville J. Armitage
ICML
2003
IEEE
13 years 12 months ago
Evolutionary MCMC Sampling and Optimization in Discrete Spaces
The links between genetic algorithms and population-based Markov Chain Monte Carlo (MCMC) methods are explored. Genetic algorithms (GAs) are well-known for their capability to opt...
Malcolm J. A. Strens