Data Mining Using Surface and Deep Agents Based on Neural Networks

被引：0
|
作者：
Kak, Subhash [1 ]
Chen, Yuhua [2 ]
Wang, Lei [2 ]
机构：
[1] Oklahoma State Univ, Stillwater, OK 74078 USA
[2] Univ Houston, Houston, TX 77004 USA
来源：
AMCIS 2010 PROCEEDINGS | 2010年
关键词：
Data mining; instantaneously trained networks; on-line learning; CLASSIFICATION;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents an approach to data mining based on an architecture that uses two kinds of neural network-based agents: (i) an instantaneously-trained surface learning agent that quickly adapts to new modes of operation; and, (ii) a deep learning agent that is very accurate within a specific regime of operation. The two agents perform complementary functions that improve the overall performance. The performance of the hybrid architecture has been compared with that of a backpropagation network for a variety of classification problems and found to be superior based on the RMS error criterion.
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页数：7
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