文章基本信息

标题：Extreme Machine Learning: Feed Forward Networks
本地全文：下载
作者：Akshay Sharma ; Babita Sandooja ; Deepika Yadav 等
期刊名称：International Journal of Advanced Research In Computer Science and Software Engineering
印刷版ISSN：2277-6451
电子版ISSN：2277-128X
出版年度：2013
卷号：3
期号：8
出版社：S.S. Mishra
摘要：The class of adaptive systems known as Artificial Neural Networks (ANN) was motivated by the amazing parallel processing capabilities of biological brains (especially the human brain). The main driving force was to re-create these abilities by constructing artificial models of the biological neuron. This paper is focused to feed -forward networks. The field has become so vast that a complete and clear-cut description of all the approaches is an enormous undertaking. The power of biological neural structures stems from the enormous number of highly interconnected simple units. The simplicity comes from the fact that, once the complex electro -chemical processes are abstracted, the resulting computation turns out to be conceptually very simple. These artificial neurons have nowadays little in common with their biological counterpart in the ANN paradigm. Rather, they are primarily used as computational devices, clearly intended to problem solving: optimization, function app roximation, classification, time-series prediction and others. In practice few elements are connected and their connectivity is low
关键词：Adaptive systems; ANN paradigm; feed-forward networks; Optimization; parallel processing