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

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From Knowledge-Based to Skill-Based Systems: Sailing as a Machine Learning Challengegoal was to make an adaptive system that would help a single handed sailor to go faster on average in a race. Presently, after five years of development and a number of sea trials, we have a commercial system available (www.robosail.com). It is a hybrid system using agent technology, machine learnin

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Next Generation Data Mining Tools: Power Laws and Self-similarity for Graphs, Streams and Traditionatribution of a company’s customers in geographical space? How long should we expect a nearest-neighbor search to take, when there are 100 attributes per patient or customer record? The traditional assumptions (uniformity, independence, Poisson arrivals, Gaussian distributions), often fail miserably.

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Support Vector Machines with Example Dependent Costsow only costs depending on the classes of the examples that are used for learning. As an extension of class dependent costs, we consider costs that are example, i.e. feature and class dependent. We present a natural cost-sensitive extension of the support vector machine (SVM) and discuss its relatio

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Abalearn: A Risk-Sensitive Approach to Self-play Learning in Abaloneabeled training examples, deep searches or exposure to competent play..Our approach is based on a reinforcement learning algorithm that is risk-seeking, since defensive players in Abalone tend to never end a game..We show that it is the risk-sensitivity that allows a successful self-play training. W

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Life Cycle Modeling of News Events Using Aging Theoryife span. A news event becomes popular with a burst of news reports, and it fades away with time. We incorporate the proposed aging theory into the traditional single-pass clustering algorithm to model life spans of news events. Experiment results show that the proposed method has fairly good perfor

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Unambiguous Automata Inference by Means of State-Merging Methodsting all examples and rejecting all counter-examples. We study unambiguous automata (UFA) inference, an intermediate framework between the hard nondeterministic automata (NFA) inference and the well known deterministic automata (DFA) inference. The search space for UFA inference is described and ori

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