Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Walter Daelemans,Bart Goethals,Katharina Morik Conference proce

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Machine Learning and Knowledge Discovery in DatabasesEuropean Conference,

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Rollout Sampling Approximate Policy Iterationg the setting as akin to a bandit problem over the states from which rollouts are performed. Our contribution is two-fold: (a) we suitably adapt existing bandit techniques for rollout management, and (b) we suggest a more appropriate statistical test for identifying states with dominating actions ea

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Inkling

Incremental Exemplar Learning Schemes for Classification on Embedded Devicesar sets of any user-defined size) and (3) robust (such that the exemplar sets generalize for other classifiers as well). Our proposed methods are as follows:.We show that our schemes efficiently incorporate new training datasets while maintaining high-quality exemplar sets of any user-defined size.

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A Collaborative Filtering Framework Based on Both Local User Similarity and Global User Similaritycan be connected through their locally similar neighbors. A weighted user graph is first constructed by using local similarity of any two users as the weight of the edge connecting them. Then the global similarity can be calculated as the maximin distance of any two nodes in the graph. Based on both

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Presbyopia

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bourgeois

Two Heads Better Than One: Pattern Discovery in Time-Evolving Multi-aspect Datarthermore, 2-heads has several other advantages as well: (a) it can be computed incrementally in a streaming fashion, (b) it has a provable error guarantee and, (c) it achieves significant compression ratio against competitors. Finally, we show experiments on real datasets, and we illustrate how 2-h