网络学习方法综述

摘要：

知识发现是从给定的数据中提取隐含的、先前未知的和潜在有用的信息。从Web文档中提取知识涉及非结构化、自由格式的文档，这些文档数量巨大且增长迅速。人工神经网络非常适合于解决从Web文档中发现知识的问题，因为经过训练的网络能够更准确、更容易地对代表文本挖掘领域的学习和测试示例进行分类。然而，由大量加权连接和激活单元组成的神经网络往往产生难以理解的文本分类模型。这个问题也可以解决到最强大的递归神经网络，其采用从隐藏或输出单元到其输入设备的反馈链路。由于反馈链接，递归神经网络能够考虑文档中的上下文。为了对数据采掘有用，自组织神经网络知识提取技术已经被探索和发展。自组织原理被用来创建一个适当的神经网络结构，并减少用于描述文本文档的特征的维数。这些原则的使用似乎很有趣，因为它们能够减少神经网络的冗余，并大大促进知识表示。

英文摘要：

Knowledge discovery is defined as non-trivial extraction of implicit, previously unknown and potentially useful information from given data. Knowledge extraction from web documents deals with unstructured, free-format documents whose number is enormous and rapidly growing. The artificial neural networks are well suitable to solve a problem of knowledge discovery from web documents because trained networks are able more accurately and easily to classify the learning and testing examples those represent the text mining domain. However, the neural networks that consist of large number of weighted connections and activation units often generate the incomprehensible and hard-to-understand models of text classification. This problem may be also addressed to most powerful recurrent neural networks that employ the feedback links from hidden or output units to their input units. Due to feedback links, recurrent neural networks are able take into account of a context in document. To be useful for data mining, self-organizing neural network techniques of knowledge extraction have been explored and developed. Self-organization principles were used to create an adequate neural-network structure and reduce a dimensionality of features used to describe text documents. The use of these principles seems interesting because ones are able to reduce a neural-network redundancy and considerably facilitate the knowledge representation.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。