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ML
2002
ACM
146views Machine Learning» more  ML 2002»
11 years 5 months ago
Kernel Matching Pursuit
Matching Pursuit algorithms learn a function that is a weighted sum of basis functions, by sequentially appending functions to an initially empty basis, to approximate a target fu...
Pascal Vincent, Yoshua Bengio
ML
2002
ACM
168views Machine Learning» more  ML 2002»
11 years 5 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
ML
2002
ACM
178views Machine Learning» more  ML 2002»
11 years 5 months ago
Metric-Based Methods for Adaptive Model Selection and Regularization
We present a general approach to model selection and regularization that exploits unlabeled data to adaptively control hypothesis complexity in supervised learning tasks. The idea ...
Dale Schuurmans, Finnegan Southey
ML
2002
ACM
220views Machine Learning» more  ML 2002»
11 years 5 months ago
Bayesian Methods for Support Vector Machines: Evidence and Predictive Class Probabilities
I describe a framework for interpreting Support Vector Machines (SVMs) as maximum a posteriori (MAP) solutions to inference problems with Gaussian Process priors. This probabilisti...
Peter Sollich
ML
2002
ACM
127views Machine Learning» more  ML 2002»
11 years 5 months ago
Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces
We examine methods for constructing regression ensembles based on a linear program (LP). The ensemble regression function consists of linear combinations of base hypotheses generat...
Gunnar Rätsch, Ayhan Demiriz, Kristin P. Benn...
ML
2002
ACM
246views Machine Learning» more  ML 2002»
11 years 5 months ago
Bayesian Clustering by Dynamics
This paper introduces a Bayesian method for clustering dynamic processes. The method models dynamics as Markov chains and then applies an agglomerative clustering procedure to disc...
Marco Ramoni, Paola Sebastiani, Paul R. Cohen
ML
2002
ACM
114views Machine Learning» more  ML 2002»
11 years 5 months ago
Building a Basic Block Instruction Scheduler with Reinforcement Learning and Rollouts
The execution order of a block of computer instructions on a pipelined machine can make a difference in running time by a factor of two or more. Compilers use heuristic schedulers...
Amy McGovern, J. Eliot B. Moss, Andrew G. Barto
ML
2002
ACM
123views Machine Learning» more  ML 2002»
11 years 5 months ago
Feature Generation Using General Constructor Functions
Most classification algorithms receive as input a set of attributes of the classified objects. In many cases, however, the supplied set of attributes is not sufficient for creatin...
Shaul Markovitch, Dan Rosenstein
ML
2002
ACM
141views Machine Learning» more  ML 2002»
11 years 5 months ago
On the Existence of Linear Weak Learners and Applications to Boosting
We consider the existence of a linear weak learner for boosting algorithms. A weak learner for binary classification problems is required to achieve a weighted empirical error on t...
Shie Mannor, Ron Meir
ML
2002
ACM
146views Machine Learning» more  ML 2002»
11 years 5 months ago
Variable Resolution Discretization in Optimal Control
Abstract. The problemof state abstractionis of centralimportancein optimalcontrol,reinforcement learning and Markov decision processes. This paper studies the case of variable reso...
Rémi Munos, Andrew W. Moore
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