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An Online Information Retrieval Systems by Means of Artificial Neural Networks

机译:人工神经网络的在线信息检索系统

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摘要

The aim of this paper is to present a new alternative to the existing Information Retrieval System (IRS) techniques, which are briefly summarized and classified. An IRS prototype has been developed with a technique based on Artificial Neural Networks which are different from those normally used for this type of applications, that is, the self-organising networks (SOM). Two types of network (radial response and multilayer perceptron) are analyzed and tested. It is concluded that, in the case of a limited number of documents and terms, the most suitable solution seems to be the Multilayer Perceptron network. The results obtained with this prototype have been positive, making the possibility of applying this technique in real size cases a cause for a certain degree of optimism.
机译:本文的目的是为现有的信息检索系统(IRS)技术提供一种新的替代方法,该技术已简要概述和分类。使用基于人工神经网络的技术开发了IRS原型,该技术不同于通常用于此类应用程序的网络,即自组织网络(SOM)。分析和测试了两种类型的网络(径向响应和多层感知器)。结论是,在文件和术语数量有限的情况下,最合适的解决方案似乎是多层Perceptron网络。用该原型获得的结果是积极的,使得在实际大小的情况下应用此技术的可能性成为一定程度乐观的原因。

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