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Combining discriminant-based classifiers: A study in decision level fusion.

机译:结合基于判别的分类器:决策级融合研究。

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Many successful pattern recognition solutions have been developed in recent years, despite the fact that pattern recognition is ill-defined and difficult due to noise and large variations in the input data. A promising approach is to fuse the decision capabilities of several classifier designs, such that they reduce the combined recognition error rate. This thesis confronts the recognition problem and proposes a method of training a Linear Fusion Network (LFN) using the Output-Reset (OR) algorithm. Training algorithms are enhanced by a closed form solution to OR. A linear network fuses the output of three types of discriminant-based classifiers: (1) Multilayer Perceptron (MLP), (2) Nearest Neighbor Classifier (NNC), and (3) Radial Basis Function (RBF) network. This framework is then applied to the task of recognition of handprinted numeral data, geometric shape data, and remote sensing data. Experimental results using OR training are compared against Minimum Classification Error (MCE) objectives.
机译:尽管由于噪声和输入数据的较大差异,模式识别的定义不明确且困难,但近年来已开发出许多成功的模式识别解决方案。一种有前途的方法是融合几种分类器设计的决策能力,从而降低组合的识别错误率。本文面对识别问题,并提出了一种使用输出重置(OR)算法训练线性融合网络(LFN)的方法。封闭式OR解决方案增强了训练算法。线性网络融合了三种基于判别式的分类器的输出:(1)多层感知器(MLP),(2)最近邻分类器(NNC)和(3)径向基函数(RBF)网络。然后将此框架应用于识别手印数字数据,几何形状数据和遥感数据的任务。将使用OR训练的实验结果与最小分类误差(MCE)目标进行比较。

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