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ML
2002
ACM
168views Machine Learning» more  ML 2002»
15 years 6 months ago
On Average Versus Discounted Reward Temporal-Difference Learning
We provide an analytical comparison between discounted and average reward temporal-difference (TD) learning with linearly parameterized approximations. We first consider the asympt...
John N. Tsitsiklis, Benjamin Van Roy
MOC
1998
136views more  MOC 1998»
15 years 6 months ago
Total variation diminishing Runge-Kutta schemes
In this paper we further explore a class of high order TVD (total variation diminishing) Runge-Kutta time discretization initialized in a paper by Shu and Osher, suitable for solvi...
Sigal Gottlieb, Chi-Wang Shu
NPL
2002
168views more  NPL 2002»
15 years 6 months ago
Reduced Rank Kernel Ridge Regression
Ridge regression is a classical statistical technique that attempts to address the bias-variance trade-off in the design of linear regression models. A reformulation of ridge regr...
Gavin C. Cawley, Nicola L. C. Talbot
SPEECH
1998
118views more  SPEECH 1998»
15 years 6 months ago
Dimensionality reduction of electropalatographic data using latent variable models
We consider the problem of obtaining a reduced dimension representation of electropalatographic (EPG) data. An unsupervised learning approach based on latent variable modelling is...
Miguel Á. Carreira-Perpiñán, ...
TEC
1998
106views more  TEC 1998»
15 years 6 months ago
Combining mutation operators in evolutionary programming
Abstract— Traditional investigations with evolutionary programming (EP) for continuous parameter optimization problems have used a single mutation operator with a parameterized p...
Kumar Chellapilla