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Titlebook: Methodologies and Intelligent Systems for Technology Enhanced Learning; Tania Di Mascio,Rosella Gennari,Fernando de la Pri Conference proc

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樓主: Hemochromatosis
31#
發(fā)表于 2025-3-27 00:55:40 | 只看該作者
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發(fā)表于 2025-3-27 04:00:36 | 只看該作者
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發(fā)表于 2025-3-27 08:40:02 | 只看該作者
Samuel González López,Aurelio López-López is, as she says, impossible to prescribe patterns for reading: ‘The only advice, indeed, that one person can give another about reading is to take no advice, to follow your own instincts, to use your own reason, to come to your own conclusions.’. Independence, Woolf confidently asserts, is ‘the mos
34#
發(fā)表于 2025-3-27 10:08:42 | 只看該作者
Margherita Brondino,Daniela Raccanello,Margherita Pasinisupport vector machines (SVMs) a.k.a. kernel machines.. The basic aim of this chapter is to give, as far as possible, a condensed (but systematic) presentation of a novel learning paradigm embodied in SVMs. Our focus Will be on the constructive learning algorithms for both the classification (patter
35#
發(fā)表于 2025-3-27 17:10:00 | 只看該作者
M. R. Cecilia,V. Cofini,Tania di Mascio,Pierpaolo Vittorinil networks. The expanding application sphere and social reach of advanced data mining raise pertinent issues of privacy and security. Present-day data mining is a progressive multidisciplinary endeavor. This inter- and multidisciplinary approach is well reflected within the field of information syst
36#
發(fā)表于 2025-3-27 20:03:07 | 只看該作者
Tania di Mascio,Alessandra Melonio,Laura Tarantino,Pierpaolo Vittoriniing both tasks of visualization and classification. Despite the different motivations, these algorithms can be cast in a graph embedding framework. In this paper we address weighted graph subspace learning methods for dimensionality reduction of bankruptcy data. The rationale behind re-embedding the
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發(fā)表于 2025-3-27 22:07:59 | 只看該作者
38#
發(fā)表于 2025-3-28 06:05:42 | 只看該作者
Barbara Giacominelli,Margherita Pasini,Rob Halling both tasks of visualization and classification. Despite the different motivations, these algorithms can be cast in a graph embedding framework. In this paper we address weighted graph subspace learning methods for dimensionality reduction of bankruptcy data. The rationale behind re-embedding the
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發(fā)表于 2025-3-28 09:17:58 | 只看該作者
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