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Titlebook: Hybrid Artificial Intelligent Systems; 8th International Co Jeng-Shyang Pan,Marios M. Polycarpou,Emilio Corcha Conference proceedings 2013

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樓主: 戰(zhàn)神
41#
發(fā)表于 2025-3-28 16:48:53 | 只看該作者
A First Approach to Deal with Imbalance in Multi-label Datasets-label datasets. Actually, the imbalance level in multi-label datasets uses to be much larger than in binary or multi-class datasets. Notwithstanding, the proposals on how to measure and deal with imbalanced datasets in multi-label classification are scarce..In this paper, we introduce two measures
42#
發(fā)表于 2025-3-28 19:41:50 | 只看該作者
Simulating a Collective Intelligence Approach to Student Team Formationrtunately, looking for optimal or near optimal teams is a costly task for humans due to the exponential number of outcomes. For this reason, in this paper we present a computer-aided policy that facilitates the automatic generation of near optimal teams based on collective intelligence, coalition st
43#
發(fā)表于 2025-3-29 00:56:46 | 只看該作者
A Counting-Based Heuristic for ILP-Based Concept Discovery Systems Although such systems have a history of more than 20 years and successful applications in various domains, they are still vulnerable to scalability and efficiency issues —mainly due to large search spaces they build. In this study we propose a heuristic to select a target instance that will lead to
44#
發(fā)表于 2025-3-29 05:16:15 | 只看該作者
Extracting Sequential Patterns Based on User Defined Criterian mining algorithms have been developed that mine the set of frequent subsequences satisfying a minimum support constraint in a transaction database. In this paper, a hybrid framework to sequential pattern mining problem is proposed which combines clustering together with a novel pattern extraction
45#
發(fā)表于 2025-3-29 10:58:30 | 只看該作者
46#
發(fā)表于 2025-3-29 11:47:47 | 只看該作者
47#
發(fā)表于 2025-3-29 16:38:39 | 只看該作者
José M. Fernández-de-Alba,Rubén Fuentes-Fernández,Juán Pavónloge, Noten, Liederbl?tter etc.) aus der Musik zu verstehen. Sie setzt sich das Ziel, aus einer Vielzahl an Daten Zusammenh?nge und Muster zu erkennen. M?gliche Einsatzgebiete sind Music Recommendation, Audio Identification, Playlist Generation oder auch die Hit Song Science – um nur wenige zu nenne
48#
發(fā)表于 2025-3-29 21:10:54 | 只看該作者
Pablo Campillo-Sanchez,Jorge J. Gómez-Sanz,Juan A. Botíaloge, Noten, Liederbl?tter etc.) aus der Musik zu verstehen. Sie setzt sich das Ziel, aus einer Vielzahl an Daten Zusammenh?nge und Muster zu erkennen. M?gliche Einsatzgebiete sind Music Recommendation, Audio Identification, Playlist Generation oder auch die Hit Song Science – um nur wenige zu nenne
49#
發(fā)表于 2025-3-30 00:53:09 | 只看該作者
50#
發(fā)表于 2025-3-30 04:20:58 | 只看該作者
Dragan Simi?,Vasa Svir?evi?,Svetlana Simi?loge, Noten, Liederbl?tter etc.) aus der Musik zu verstehen. Sie setzt sich das Ziel, aus einer Vielzahl an Daten Zusammenh?nge und Muster zu erkennen. M?gliche Einsatzgebiete sind Music Recommendation, Audio Identification, Playlist Generation oder auch die Hit Song Science – um nur wenige zu nenne
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