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.

引用

页数：7

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