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ICML
2001
IEEE
14 years 6 months ago
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
This paper presents an active learning method that directly optimizes expected future error. This is in contrast to many other popular techniques that instead aim to reduce versio...
Nicholas Roy, Andrew McCallum
ICML
2001
IEEE
14 years 6 months ago
Off-Policy Temporal Difference Learning with Function Approximation
We introduce the first algorithm for off-policy temporal-difference learning that is stable with linear function approximation. Off-policy learning is of interest because it forms...
Doina Precup, Richard S. Sutton, Sanjoy Dasgupta
ICML
2001
IEEE
14 years 6 months ago
Breeding Decision Trees Using Evolutionary Techniques
Athanassios Papagelis, Dimitrios Kalles
ICML
2001
IEEE
14 years 6 months ago
Coupled Clustering: a Method for Detecting Structural Correspondence
Zvika Marx, Ido Dagan, Joachim M. Buhmann
ICML
2001
IEEE
14 years 6 months ago
Inducing Partially-Defined Instances with Evolutionary Algorithms
This paper addresses the issue of reducing the storage requirements on Instance-Based Learning algorithms. Algorithms proposed by other researches use heuristics to prune instance...
Josep Maria Garrell i Guiu, Xavier Llorà
ICML
2001
IEEE
14 years 6 months ago
Using EM to Learn 3D Models of Indoor Environments with Mobile Robots
This paper describes an algorithm for generating compact 3D models of indoor environments with mobile robots. Our algorithm employs the expectation maximization algorithm to fit a...
Yufeng Liu, Rosemary Emery, Deepayan Chakrabarti, ...
ICML
2001
IEEE
14 years 6 months ago
Learning with the Set Covering Machine
We generalize the classical algorithms of Valiant and Haussler for learning conjunctions and disjunctions of Boolean attributes to the problem of learning these functions over arb...
Mario Marchand, John Shawe-Taylor
ICML
2001
IEEE
14 years 6 months ago
Estimating a Kernel Fisher Discriminant in the Presence of Label Noise
Data noise is present in many machine learning problems domains, some of these are well studied but others have received less attention. In this paper we propose an algorithm for ...
Bernhard Schölkopf, Neil D. Lawrence
ICML
2001
IEEE
14 years 6 months ago
An Improved Predictive Accuracy Bound for Averaging Classifiers
We present an improved bound on the difference between training and test errors for voting classifiers. This improved averaging bound provides a theoretical justification for popu...
John Langford, Matthias Seeger, Nimrod Megiddo