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» Evolutionary Algorithms for the Satisfiability Problem
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ICML
1998
IEEE
16 years 2 months ago
Q2: Memory-Based Active Learning for Optimizing Noisy Continuous Functions
This paper introduces a new algorithm, Q2, foroptimizingthe expected output ofamultiinput noisy continuous function. Q2 is designed to need only a few experiments, it avoids stron...
Andrew W. Moore, Jeff G. Schneider, Justin A. Boya...
EH
1999
IEEE
351views Hardware» more  EH 1999»
15 years 5 months ago
Evolvable Hardware or Learning Hardware? Induction of State Machines from Temporal Logic Constraints
Here we advocate an approach to learning hardware based on induction of finite state machines from temporal logic constraints. The method involves training on examples, constraint...
Marek A. Perkowski, Alan Mishchenko, Anatoli N. Ch...
128
Voted
HIS
2007
15 years 3 months ago
Pareto-based Multi-Objective Machine Learning
—Machine learning is inherently a multiobjective task. Traditionally, however, either only one of the objectives is adopted as the cost function or multiple objectives are aggreg...
Yaochu Jin
CN
2007
149views more  CN 2007»
15 years 1 months ago
Adaptive congestion protocol: A congestion control protocol with learning capability
There is strong evidence that the current implementation of TCP will perform poorly in future high speed networks. To address this problem many congestion control protocols have b...
Marios Lestas, Andreas Pitsillides, Petros A. Ioan...
ICC
2007
IEEE
15 years 8 months ago
Optical WDM Network Planning Using Heterogeneous Multi-granularity OXCs
Multigranular optical WDM network aims to reduce network cost by grouping multiple wavelengths and then switching those wavelengths together at waveband or fiber levels. To configu...
Hong-Hsu Yen, Frank Yeong-Sung Lin, Steven S. W. L...