Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Toon Calders,Floriana Esposito,Rosa Meo Conference proceedings

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Machine Learning and Knowledge Discovery in Databases978-3-662-44848-9Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Fast Nearest Neighbor Search on Large Time-Evolving Graphsformation as the graph changes. At query time, we combine this information from a small set of relevant clusters and compute . scores efficiently. We validate the effectiveness of our method on large real-world graphs from diverse domains. To the best of our knowledge, this is one of the few works t

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Deconstructing Kernel Machinesthat with .(.) number of queries, the type of kernel function and the (kernel) subspace spanned by the support vectors can be determined. In particular, for polynomial kernels, additional .(..) queries are sufficient to reconstruct the entire decision boundary, providing a set of quasi-support vecto

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Kernel Principal Geodesic Analysis methods for (i) dimensionality reduction and (ii) clustering using mixture-model fitting. The results, on simulated and real-world data, show that kPGA-based methods perform favorably relative to their kPCA-based analogs.

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Knowledge-Powered Deep Learning for Word Embedding knowledge to define new basis for word representation, provide additional input information, and serve as auxiliary supervision in deep learning, respectively. Experiments on an analogical reasoning task, a word similarity task, and a word completion task have all demonstrated that knowledge-powere

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Active Learning for Support Vector Machines with Maximum Model Changeorder to provide a theoretical justification for margin-based strategies. Extensive experimental results on various benchmark data sets from UCI machine learning repository have demonstrated the effectiveness and efficiency of the proposed method.

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Seyyed Abbas Hosseini,Hamid R. Rabiee,Hassan Hafez,Ali Soltani-Faraniten Statistikkenntnissen können das Buch mit seinen umfangreichen Übersichten und genauen Ablaufplänen zu den statistischen Verfahren als komfortable Erinnerungshilfe und Nachschlagewerk verwenden.978-3-662-56081-5978-3-662-56082-2Series ISSN 0937-7433 Series E-ISSN 2512-5214

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Mohsen Ali,Muhammad Rushdi,Jeffrey Hoten Statistikkenntnissen können das Buch mit seinen umfangreichen Übersichten und genauen Ablaufplänen zu den statistischen Verfahren als komfortable Erinnerungshilfe und Nachschlagewerk verwenden.978-3-662-56081-5978-3-662-56082-2Series ISSN 0937-7433 Series E-ISSN 2512-5214