Combination of modified BPNN algorithms and an efficient feature selection method for text categorization

This paper proposes new modified methods for back propagation neural networks and uses semantic feature space to improve categorization performance and efficiency. The standard back propagation neural network (BPNN) has the drawbacks of slow learning and getting trapped in local minima, leading to a network with poor performance and efficiency. In this paper, we propose two methods to modify the standard BPNN and adopt the semantic feature space (SFS) method to reduce the number of dimensions as well as construct latent semantics between terms. The experimental results show that the modified methods enhanced the performance of the standard BPNN and were more efficient than the standard BPNN. The SFS method cannot only greatly reduce the dimensionality, but also enhances performance and can therefore be used to further improve text categorization systems precisely and efficiently.     

从东西部地区差距评价西部大开发战略实施绩效——基于主成份分析法的视角

本文在比较分析1999年西部大开发战略实施以来东西部地区六个方面差异的基础上,通过构建西部大开发绩效评价指标体系,运用主成份分析法,经过量化分析,指出市场政策差异、市场容量差异和市场体制差异是目前造成东西部地区发展差距逐渐扩大的主要因素。因此,缩小地区差距应在国家宏观政策的引导下,以市场为切入点,发挥市场机制在东西部地区资源配置中的基础作用。