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Titlebook: Machine Learning: ECML 2001; 12th European Confer Luc Raedt,Peter Flach Conference proceedings 2001 Springer-Verlag Berlin Heidelberg 2001

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楼主: Tyler
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Lazy Induction of Descriptions for Relational Case-Based Learningadequate for domains where cases are best represented by relations among entities. LID is able to 1) define a ., a symbolic description of what is shared between a problem and precedent cases; and 2) assess the importance of the relations involved in a similitude term with respect to the purpose of
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Wrapping Web Information Providers by Transducer Inductiont relevant information from HTML responses and to annotate it with userdefined labels. A number of approaches exploit the methods of machine learning to induce instances of certain wrapper classes, by assuming the tabular structure of HTML responses and by observing the regularity of extracted fragm
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Speeding Up Relational Reinforcement Learning through the Use of an Incremental First Order Decisionle the learning system to exploit structural knowledge about the application domain..This paper discusses an improvement of the original RRL. We introduce a fully incremental first order decision tree learning algorithm TG and integrate this algorithm in the RRL system to form RRL-TG. We demonstrate
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Analysis of the Performance of AdaBoost.M2 for the Simulated Digit-Recognition-Examplecognition example, a simulated data set with attribute noise. Although the final model is both simple and complex enough, boosting fails to reach the Bayes error. A detailed analysis shows some characteristics of the boosting trials which influence the lack of fit.
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Iterative Double Clustering for Unsupervised and Semi-supervised Learningnim and Tishby that exhibited impressive performance on text categorization tasks [1]. Using synthetically gener ated data we empirically demonstrate that whenever the DC procedure is successful in recovering some of the structure hidden in the data, the extended IDC procedure can incrementally comp
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