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Titlebook: Artificial Intelligence and Soft Computing; 19th International C Leszek Rutkowski,Rafa? Scherer,Jacek M. Zurada Conference proceedings 2020

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31#
發(fā)表于 2025-3-26 22:04:16 | 只看該作者
A Density-Based Prototype Selection Approach mining and machine learning tasks. In this paper, we propose a density-based approach for selecting prototypes. Firstly, it finds the density peaks in each dimension of the dataset. After that, it builds clusters of objects around these peaks. Finally, it extracts a prototype that represents each c
32#
發(fā)表于 2025-3-27 01:06:21 | 只看該作者
FlexTrustRank: A New Approach to Link Spam Combatingnk constituted a break-through as a method of webpage evaluation and was in use since then for multiple web search engines. This concept has been subject to attacks by malicious web linkage. As an counteraction, the so-called TrustRank was developed that instead of trusting all webpage developers it
33#
發(fā)表于 2025-3-27 08:26:00 | 只看該作者
34#
發(fā)表于 2025-3-27 10:49:19 | 只看該作者
35#
發(fā)表于 2025-3-27 17:39:03 | 只看該作者
A Framework for e-Recruitment Recommender Systemsolutionize how organizations execute talent search and acquisition. This paper briefly discusses the e-Recruitment problem and presents a framework together with three recommendation models aiming to overcome the particular challenges presented in this field.
36#
發(fā)表于 2025-3-27 19:26:28 | 只看該作者
37#
發(fā)表于 2025-3-27 21:55:22 | 只看該作者
38#
發(fā)表于 2025-3-28 02:18:38 | 只看該作者
0302-9743 computer vision, image and speech analysis; data mining; various problems of artificial intelligence; agent systems, robotics and control..*The conference was held virtually due to the COVID-19 pandemic..978-3-030-61533-8978-3-030-61534-5Series ISSN 0302-9743 Series E-ISSN 1611-3349
39#
發(fā)表于 2025-3-28 09:10:04 | 只看該作者
40#
發(fā)表于 2025-3-28 11:14:38 | 只看該作者
The Sreni-Drawing the Legacy of CSR in Indiaed not only to images but also to vectors of features arising in other applications. Additionally, this idea can be combined with any classifier that is able to take into account a priori probabilities. We provide an analysis when this approach leads to the reduction of the classification errors in
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