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ECML
2004
Springer
13 years 9 months ago
Experiments in Value Function Approximation with Sparse Support Vector Regression
Abstract. We present first experiments using Support Vector Regression as function approximator for an on-line, sarsa-like reinforcement learner. To overcome the batch nature of S...
Tobias Jung, Thomas Uthmann
ECML
2004
Springer
13 years 9 months ago
SWITCH: A Novel Approach to Ensemble Learning for Heterogeneous Data
The standard framework of machine learning problems assumes that the available data is independent and identically distributed (i.i.d.). However, in some applications such as image...
Rong Jin, Huan Liu
ECML
2004
Springer
13 years 9 months ago
Fisher Kernels for Logical Sequences
One approach to improve the accuracy of classifications based on generative models is to combine them with successful discriminative algorithms. Fisher kernels were developed to c...
Kristian Kersting, Thomas Gärtner
ECML
2004
Springer
13 years 9 months ago
Constructive Induction for Classifying Time Series
Mohammed Waleed Kadous, Claude Sammut
ECML
2004
Springer
13 years 9 months ago
Estimating Attributed Central Orders: An Empirical Comparison
Toshihiro Kamishima, Hideto Kazawa, Shotaro Akaho
ECML
2004
Springer
13 years 9 months ago
Analyzing Multi-agent Reinforcement Learning Using Evolutionary Dynamics
In this paper, we show how the dynamics of Q-learning can be visualized and analyzed from a perspective of Evolutionary Dynamics (ED). More specifically, we show how ED can be use...
Pieter Jan't Hoen, Karl Tuyls
ECML
2004
Springer
13 years 9 months ago
Model Approximation for HEXQ Hierarchical Reinforcement Learning
HEXQ is a reinforcement learning algorithm that discovers hierarchical structure automatically. The generated task hierarchy repthe problem at different levels of abstraction. In ...
Bernhard Hengst
ECML
2004
Springer
13 years 9 months ago
Adaptive Online Time Allocation to Search Algorithms
Matteo Gagliolo, Viktor Zhumatiy, Jürgen Schm...
ECML
2004
Springer
13 years 9 months ago
An Analysis of Stopping and Filtering Criteria for Rule Learning
Abstract. In this paper, we investigate the properties of commonly used prepruning heuristics for rule learning by visualizing them in PN-space. PN-space is a variant of ROC-space,...
Johannes Fürnkranz, Peter A. Flach