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» Optimization by Stochastic Continuation
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AI
2002
Springer
15 years 4 months ago
Multiagent learning using a variable learning rate
Learning to act in a multiagent environment is a difficult problem since the normal definition of an optimal policy no longer applies. The optimal policy at any moment depends on ...
Michael H. Bowling, Manuela M. Veloso
ECIR
2010
Springer
15 years 2 months ago
Maximum Margin Ranking Algorithms for Information Retrieval
Abstract. Machine learning ranking methods are increasingly applied to ranking tasks in information retrieval (IR). However ranking tasks in IR often differ from standard ranking t...
Shivani Agarwal, Michael Collins
COCO
1994
Springer
140views Algorithms» more  COCO 1994»
15 years 8 months ago
Random Debaters and the Hardness of Approximating Stochastic Functions
A probabilistically checkable debate system (PCDS) for a language L consists of a probabilisticpolynomial-time veri er V and a debate between Player 1, who claims that the input x ...
Anne Condon, Joan Feigenbaum, Carsten Lund, Peter ...
RSS
2007
136views Robotics» more  RSS 2007»
15 years 5 months ago
The Stochastic Motion Roadmap: A Sampling Framework for Planning with Markov Motion Uncertainty
— We present a new motion planning framework that explicitly considers uncertainty in robot motion to maximize the probability of avoiding collisions and successfully reaching a ...
Ron Alterovitz, Thierry Siméon, Kenneth Y. ...
ACL
1998
15 years 5 months ago
Machine Translation with a Stochastic Grammatical Channel
We introduce a stochastic grammatical channel model for machine translation, that synthesizes several desirable characteristics of both statistical and grammatical machine transla...
Dekai Wu, Hongsing Wong