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HONNs with Extreme learning Machine to Handle Incomplete Datasets

Citation

Xu, S, HONNs with Extreme learning Machine to Handle Incomplete Datasets, Artificial Higher Order Neural Networks for Modeling and Simulation, Information Science Reference, J Gamon (ed), United States of America, pp. 276-292. ISBN 978-1-4666-2175-6 (2012) [Research Book Chapter]

Copyright Statement

Copyright 2013 IGI Global

DOI: doi:10.4018/978-1-4666-2175-6.ch013

Abstract

An Extreme Learning Machine (ELM) randomly chooses hidden neurons and analytically determines the output weights (Huang, et al., 2005, 2006, 2008). With the ELM algorithm, only the connection weights between hidden layer and output layer are adjusted. The ELM algorithm tends to generalize better at a very fast learning speed: it can learn thousands of times faster than conventionally popular learning algorithms (Huang, et al., 2006). Artificial Neural Networks (ANNs) have been widely used as powerful information processing models and adopted in applications such as bankruptcy prediction, predicting costs, forecasting revenue, forecasting share prices and exchange rates, processing docu- ments, and many more. Higher Order Neural Networks (HONNs) are ANNs in which the net input to a computational neuron is a weighted sum of products of its inputs. Real life data are not usually perfect. They contain wrong, incomplete, or vague data. Hence, it is usual to find missing data in many infor- mation sources used. Missing data is a common problem in statistical analysis (Little & Rubin, 1987). This chapter uses the Extreme Learning Machine (ELM) algorithm for HONN models and applies it in several significant business cases, which involve missing datasets. The experimental results demonstrate that HONN models with the ELM algorithm offer significant advantages over standard HONN models, such as faster training, as well as improved generalization abilities.

Item Details

Item Type:Research Book Chapter
Research Division:Information and Computing Sciences
Research Group:Artificial Intelligence and Image Processing
Research Field:Neural, Evolutionary and Fuzzy Computation
Objective Division:Information and Communication Services
Objective Group:Computer Software and Services
Objective Field:Computer Software and Services not elsewhere classified
Author:Xu, S (Dr Shuxiang Xu)
ID Code:82173
Year Published:2012
Deposited By:Computing and Information Systems
Deposited On:2013-01-17
Last Modified:2017-11-13
Downloads:5 View Download Statistics

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