A rough set based corner classification neural network, the Rough-CC4, is presented to solve document classification problems such as document representation of different document sizes, document feature selection and...
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A rough set based corner classification neural network, the Rough-CC4, is presented to solve document classification problems such as document representation of different document sizes, document feature selection and document feature encoding. In the Rough-CC4, the documents are described by the equivalent classes of the approximate words. By this method, the dimensions representing the documents can be reduced, which can solve the precision problems caused by the different document sizes and also blur the differences caused by the approximate words. In the Rough-CC4, a binary encoding method is introduced, through which the importance of documents relative to each equivalent class is encoded. By this encoding method, the precision of the Rough-CC4 is improved greatly and the space complexity of the Rough-CC4 is reduced. The Rough-CC4 can be used in automatic classification of documents.
由于SNMP(S imp le Network M anagem ent Protocol)管理系统在配置管理方面具有一定弱点。Net-conf协议被认为是解决网络配置管理问题较为有效的方法。文中采用XML Schem a描述网络管理系统的数据模型,并且为了保证Netconf配置操作的...
详细信息
由于SNMP(S imp le Network M anagem ent Protocol)管理系统在配置管理方面具有一定弱点。Net-conf协议被认为是解决网络配置管理问题较为有效的方法。文中采用XML Schem a描述网络管理系统的数据模型,并且为了保证Netconf配置操作的事务可靠性,在Netconf的概念模型基础之上定义了网络管理应用操作层。同时也描述了基于Netconf的网络管理系统的系统结构和实现模型,提出了用于建立网络管理应用系统的设计模式,最后介绍了开发基于XML网络管理系统的经验。
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