Titlebook: Machine Learning: ECML 2003; 14th European Confer Nada Lavrač,Dragan Gamberger,Ljupčo Todorovski Conference proceedings 2003 Springer-Verla

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Combined Optimization of Feature Selection and Algorithm Parameters in Machine Learning of Languagee which machine learning algorithms have the ‘right bias’ to solve specific natural language processing tasks, and (ii) to investigate which sources of information add to accuracy in a learning approach. Using automatic word sense disambiguation as an example task, we show that with the methodology

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Iteratively Extending Time Horizon Reinforcement Learningmating the so-called .-function from a sample of four-tuples (.., .. , .., ..) where .. denotes the system state at time ., .. the control action taken, .. the instantaneous reward obtained and .. the successor state of the system, and by determining the optimal control from the .-function. Classica

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Volume under the ROC Surface for Multi-class Problemsbeen elected as a better way to evaluate classifiers than predictive accuracy or error and has also recently used for evaluating probability estimators. However, the extension of the Area Under the ROC Curve for more than two classes has not been addressed to date, because of the complexity and elus

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A New Way to Introduce Knowledge into Reinforcement Learningduce the learning time of the Q-learning algorithm. This introduction of initial knowledge is done by constraining the set of available actions in some states. But at the same time, we can formulate that if the agent is in some particular states (called exception states), we have to relax those cons

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COllective INtelligence with Sequences of Actionsd to sub optimal solutions as agents compete or interfere. The COllective INtelligence (COIN) framework of Wolpert et al. proposes an engineering solution for MASs where agents learn to focus on actions which support a common task. As a case study, we investigate the performance of COIN for represen

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