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Titlebook: Soft Computing in Data Science; Third International Azlinah Mohamed,Michael W. Berry,Bee Wah Yap Conference proceedings 2017 Springer Natu

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樓主: ODDS
31#
發(fā)表于 2025-3-27 00:35:16 | 只看該作者
Conference proceedings 2017n topical sections on?deep learning and real-time classification; image feature classification and extraction; classification, clustering, visualization; applications of machine learning; data visualization; fuzzy logic; prediction models and e-learning; text and sentiment analytics..
32#
發(fā)表于 2025-3-27 03:52:20 | 只看該作者
Evaluation of Randomized Variable Translation Wavelet Neural Networksusing benchmark data form UCI machine learning datasets were conducted. The experimental results show that RVT-WNN can work on a broad range of applications from the small size up to the large size with comparable performance to other well-known classifiers.
33#
發(fā)表于 2025-3-27 05:42:01 | 只看該作者
34#
發(fā)表于 2025-3-27 10:57:11 | 只看該作者
Conference proceedings 2017nesia, November 27-28, 2017..The 26 revised full papers presented were carefully reviewed and selected from 68 submissions. The papers are organized in topical sections on?deep learning and real-time classification; image feature classification and extraction; classification, clustering, visualizati
35#
發(fā)表于 2025-3-27 15:41:57 | 只看該作者
36#
發(fā)表于 2025-3-27 18:26:46 | 只看該作者
37#
發(fā)表于 2025-3-28 00:10:51 | 只看該作者
Modeling of the Gaussian-Based Component Analysis on the Kernel Space to Extract Face Imageg sets, 90.83% for three training sets, and 92.38% for four training sets on the YALE database. On the CAI-UTM database, the proposed method could classify correctly by 83.75%, 85.57%, and 87.33% for two, three, and four training sets respectively. The comparison results show that the results of the proposed approach outperformed to other methods.
38#
發(fā)表于 2025-3-28 02:34:14 | 只看該作者
39#
發(fā)表于 2025-3-28 08:28:37 | 只看該作者
Evaluation of Randomized Variable Translation Wavelet Neural Networkst learning algorithms have been proposed such as backpropagation and hybrid wavelet-particle swarm optimization. However, most of them are time costly. This paper proposed a new learning mechanism for VT-WNN using random weights. To validate the performance of randomized VT-WNN, several experiments
40#
發(fā)表于 2025-3-28 13:01:44 | 只看該作者
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