WIT Press


Alleviating The Complexity Of The Combinatorial Neural Model Using A Committee Machine

Price

Free (open access)

Paper DOI

10.2495/DATA000361

Volume

25

Pages

7

Published

2000

Size

739 kb

Author(s)

H.A. do Prado, K.F. Machado & P. M. Engel

Abstract

Knowledge Discovery from Databases (KDD) can be seen as a set of computer- aided knowledge discovery techniques scaled up to very large databases. By this way, the old process of discovery has experienced amazing improvements by: (a) allowing well-known Machine Learning and Statistical algorithms run for larger data sets with good performance; and (b) making easier tasks like data gathering and cleansing, parameter and model selection, and so on. In this paper we take the Combinatorial Neural Model (CNM), proposed by Machado and Rocha ([6], [7], and [8]), and explore the adoption of a committee machine to cope with the complexity problem presen

Keywords