【作者】 马树华；

【导师】 王熙照；

【作者基本信息】 河北大学 ， 基础数学， 2005， 硕士

【摘要】 近年来,在模式识别、机器学习等领域,信息融合技术得到了迅速发展和广泛应用。信息融合包括三个阶段:数据融合、特征融合和决策融合,大量的分类器融合方法都是决策融合。考虑到分类器之间存在着交互影响,本文使用Choquet模糊积分这个融合算子,将已训练好的神经网络作为分类器进行融合,并将模糊积分融合系统的分类精度与单个分类器的分类精度进行比较。由于Choquet模糊积分的计算可以转化成模糊测度的线性组合,关于模糊测度是可微的,可以使用标准的优化技术来确定模糊测度。本文使用线性规划和二次规划来确定模糊测度,并在pima数据库上进行测试。实验发现,基于二次规划确定的模糊测度的系统融合精度要高于线性规划确定的模糊测度的系统融合精度,但是二者的时间复杂度却相差不大。更多还原

【Abstract】 Recently, in the fields of pattern recognition and machine learning ,the technology of information fusion develops very fast and expands its wide application. There are three stages in information fusion: data fusion; character fusion and decision fusion. A lot of approaches to classifier fusion are decision fusion. In many realistic cases, multiple classifiers are not independent but have interaction. In this thesis, we use the choquet fuzzy integral operator to fuse the different neural network classifiers which have been trained in advance. We compare the fusion system’s classification accuracy and an individual classifier’s classification accuracy. The calculation of the choquet integral can be transferred into the linear combination about the fuzzy measure, which is differentiable. This quality allows us to use the standard optimization technology to determine the fuzzy measure. In this thesis, we use the linear programming and quadratic programming to determine the fuzzy measure, these methods are tested in pima data set. The experiments shows that: the fusion accuracy based on the fuzzy measure which is determined by the quadratic programming is higher than the accuracy based on the fuzzy measure which is determined by the linear programming. There are almost no different in time and space complexity.更多还原