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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
51#
發(fā)表于 2025-3-30 10:27:17 | 只看該作者
Tiago Oliveira,Jo?o Neves,Ernesto Barbosa,Paulo Novaisloge, 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
52#
發(fā)表于 2025-3-30 13:53:30 | 只看該作者
Robert Burduk,Pawel Trajdosas been designed and implemented which employs distributed blob store, custom compression, and custom query algorithm, including filtering, joins and group by. The system has been in operation at eBay for years and is described in [.]. However, large scale ingestion of data to a distributed blob sto
53#
發(fā)表于 2025-3-30 18:02:37 | 只看該作者
54#
發(fā)表于 2025-3-30 23:20:33 | 只看該作者
55#
發(fā)表于 2025-3-31 01:36:16 | 只看該作者
Juan M. Alberola,Elena del Val,Victor Sanchez-Anguix,Vicente Julianthe quality of feature extraction over the text corpus. For supervised learning over text documents, the TF-IDF (Term Frequency-Inverse Document Frequency) weighting factor is one of the most frequently used features in text classification. In this paper, we address two known limitations of TF-IDF b
56#
發(fā)表于 2025-3-31 07:56:42 | 只看該作者
Alev Mutlu,P?nar Karagoz,Yusuf Kavurucuricultural data are usually collected through various sources, such as: Internet of Thing (IoT), sensors, satellites, weather stations, robots, farm equipment, farmers and agribusinesses, etc. Besides, agricultural datasets are very large, complex, unstructured, heterogeneous, non-standardized, and
57#
發(fā)表于 2025-3-31 10:13:58 | 只看該作者
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