Mining Data by Query-Based Error-Propagation
Resource
Lecture Notes in Computer Science, 3610, 1224-1233
Journal
Lecture Notes in Computer Science
Journal Volume
3610
Journal Issue
PART I
Pages
1224-1233
Date Issued
2005
Date
2005
Author(s)
Abstract
Neural networks have advantages of the high tolerance to noisy data as well as the ability to classify patterns having not been trained. While being applied in data mining, the time required to induce models from large data sets are one of the most important considerations. In this paper, we introduce a query-based learning scheme to improve neural networks' performance in data mining. Results show that the proposed algorithm can significantly reduce the training set cardinality. Additionally, the quality of training results can be also ensured. Our future work is to apply this concept to other data mining schemes and applications. © Springer-Verlag Berlin Heidelberg 2005.
Type
journal article
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