牢骚
发表于 2025-3-28 15:52:20
Multi-Agent Data Mining using Evolutionary Computing,gorithms that build feature-vector-based classifiers in the form of rule sets. With the tremendous explosion in the amount of data being amassed by organizations of today, it is critically important that data mining techniques are able to process such data efficiently. We present the Distributed Lea
引起
发表于 2025-3-28 22:36:17
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obviate
发表于 2025-3-28 23:22:17
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Vital-Signs
发表于 2025-3-29 05:42:12
Diversity and Neuro-Ensemble,ns. It has been shown that combining different neural networks can improve the generalization ability of learning machines. Diversity of the ensemble’s members plays a key role in minimizing the combined bias and variance of the ensemble. In this chapter, we compare between different mechanisms and
GNAT
发表于 2025-3-29 10:28:25
Unsupervised Niche Clustering: Discovering an Unknown Number of Clusters in Noisy Data Sets,ionary techniques have been used with success as global searchers in difficult problems, particularly in the optimization of non-differentiable functions. Hence, they can improve clustering. However, existing . clustering techniques suffer from one or more of the following shortcomings: (i) they are
土坯
发表于 2025-3-29 12:52:49
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跑过
发表于 2025-3-29 19:36:16
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dictator
发表于 2025-3-29 23:12:09
Microarray Data Mining with Evolutionary Computation,umber of gene expressions coupled with analysis over a time course, provides an immense space of possible relations. Some small portion of this space contains information that is of extreme value to modern biomedicine in terms of proper diagnosis and treatment of many diseases. Classical methods of
THE
发表于 2025-3-30 01:18:24
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LUDE
发表于 2025-3-30 06:11:46
https://doi.org/10.1057/9780230306851ion systems from the view point of its components. Then we propose a decompositional rule extraction method based on RBF neural networks. In the proposed rule extraction method, rules are extracted from trained RBF neural networks with class-dependent features. GA is used to determine the feature su