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Titlebook: Discovery Science; 15th International C Jean-Gabriel Ganascia,Philippe Lenca,Jean-Marc Pet Conference proceedings 2012 Springer-Verlag Berl

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發(fā)表于 2025-3-21 16:38:49 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Discovery Science
副標(biāo)題15th International C
編輯Jean-Gabriel Ganascia,Philippe Lenca,Jean-Marc Pet
視頻videohttp://file.papertrans.cn/282/281043/281043.mp4
概述Up-to-date results.Fast-track conference proceedings.State-of-the-art research
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Discovery Science; 15th International C Jean-Gabriel Ganascia,Philippe Lenca,Jean-Marc Pet Conference proceedings 2012 Springer-Verlag Berl
描述This book constitutes the refereed proceedings of the 15th International Conference on Discovery Science, DS 2012, held in Lyon, France, in October 2012. The 22 papers presented in this volume were carefully reviewed and selected from 46 submissions. The field of discovery science aims at inducing and validating new scientific hypotheses from data. The scope of this conference includes the development and analysis of methods for automatic scientific knowledge discovery, machine learning, intelligent data analysis, theory of learning, tools for supporting the human process of discovery in science, as well as their application to knowledge discovery.
出版日期Conference proceedings 2012
關(guān)鍵詞XML mining; autonomous exploration; data mining; huge network; reinforcement learning; algorithm analysis
版次1
doihttps://doi.org/10.1007/978-3-642-33492-4
isbn_softcover978-3-642-33491-7
isbn_ebook978-3-642-33492-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2012
The information of publication is updating

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發(fā)表于 2025-3-21 23:45:28 | 只看該作者
Recent Developments in Pattern Miningquent itemsets have been proposed. These exhaustive algorithms, however, all suffer from the pattern explosion problem. Depending on the minimal support threshold, even for moderately sized databases, millions of patterns may be generated. Although this problem is by now well recognized in te patter
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Large Scale Spectral Clustering Using Resistance Distance and Spielman-Teng Solverse price for this promise is the computational cost .(..) for computing the eigen-decomposition of the graph Laplacian matrix - so far a necessary subroutine for spectral clustering. In this paper we bypass the eigen-decomposition of the original Laplacian matrix by leveraging the recently introduced
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發(fā)表于 2025-3-22 09:49:45 | 只看該作者
Prediction of Quantiles by Statistical Learning and Application to GDP Forecastingnctions. In a first time, we show that the Gibbs estimator is able to predict as well as the best predictor in a given family for a wide set of loss functions. In particular, using the quantile loss function of [1], this allows to build confidence intervals. We apply these results to the problem of
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發(fā)表于 2025-3-22 15:41:12 | 只看該作者
Policy Search in a Space of Simple Closed-form Formulas: Towards Interpretability of Reinforcement Llgorithm over a space of simple closed-form formulas that are used to rank actions. We formalize the search for a high-performance policy as a multi-armed bandit problem where each arm corresponds to a candidate policy canonically represented by its shortest formula-based representation. Experiments
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發(fā)表于 2025-3-22 17:46:02 | 只看該作者
Towards Finding Relational Redescriptionstional dataset. By extending redescription mining beyond propositional and real-valued attributes, it provides a powerful tool to match different relational descriptions of the same concept. As a first step towards solving this general task, we introduce an efficient algorithm that mines one descrip
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A Trim Distance between Positions in Nucleotide Sequencesg the indices of nucleotide sequences as labels of leaves with the nucleotides occurring in a position, we formulate a . between two positions in nucleotide sequences as the LCA-preserving distance between the trimmed phylogenetic trees according to nucleotides occurring in the positions. Finally, w
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發(fā)表于 2025-3-23 05:39:03 | 只看該作者
Data Squashing for HSV Subimages by an Autonomous Mobile Robotakes during a navigation of dozens of minutes. The subimages are managed according to a similarity measure between a pair of subimages, which is based on a method for quantizing HSV colors. The data index structure has been inspired by the CF tree of BIRCH, which is an early work in data squashing,
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