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» Multiagent learning using a variable learning rate
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UAI
1998
15 years 6 months ago
Learning the Structure of Dynamic Probabilistic Networks
Dynamic probabilistic networks are a compact representation of complex stochastic processes. In this paper we examine how to learn the structure of a DPN from data. We extend stru...
Nir Friedman, Kevin P. Murphy, Stuart J. Russell
SODA
2000
ACM
85views Algorithms» more  SODA 2000»
15 years 6 months ago
Improved bounds on the sample complexity of learning
We present a new general upper bound on the number of examples required to estimate all of the expectations of a set of random variables uniformly well. The quality of the estimat...
Yi Li, Philip M. Long, Aravind Srinivasan
203
Voted
CVPR
2011
IEEE
15 years 11 days ago
Learning and Matching Multiscale Template Descriptors for Real-Time Detection, Localization and Tracking
We describe a system to learn an object template from a video stream, and localize and track the corresponding object in live video. The template is decomposed into a number of lo...
Taehee Lee, Stefano Soatto
TNN
1998
111views more  TNN 1998»
15 years 4 months ago
Asymptotic distributions associated to Oja's learning equation for neural networks
— In this paper, we perform a complete asymptotic performance analysis of the stochastic approximation algorithm (denoted subspace network learning algorithm) derived from Oja’...
Jean Pierre Delmas, Jean-Francois Cardos
MLMTA
2003
15 years 6 months ago
Using a Two-Layered Case-Based Reasoning for Prediction in Soccer Coach
Abstract— The prediction of the future states in MultiAgent Systems has been a challenging problem since the begining of MAS. Robotic soccer is a MAS environment in which the pre...
Mazda Ahmadi, Abolfazl Keighobadi Lamjiri, Mayssam...