Hierarchical Polytope ARTMAP for Supervised Learning  

作　　者：廖鑫鹏 吴永强 韩崇昭 

机构地区：[1]Southwest Research Institute of Electronics and Telecommunication Technology [2]College of Electronics and Information Engineering,Xi'an Jiaotong University

出　　处：《Journal of Computer Science & Technology》2010年第5期1071-1082,共12页计算机科学技术学报（英文版）

基　　金：Supported by the National Basic Research 973 Program of China under Grant No.2007CB311006.

摘　　要：The recent Polytope ARTMAP（PTAM） suggests that irregular polytopes are more flexible than the predefined category geometries to approximate the borders among the desired output predictions.However,category expansion and adjustment steps without statistical information make PTAM not robust to noise and category overlap.In order to push the learning problem towards Structural Risk Minimization（SRM）,this paper proposes Hierarchical Polytope ARTMAP （HPTAM） to use a hierarchical structure with different levels,which are determined by the complexity of regions incorporating the input pattern.Besides,overlapping of simplexes from the same desired prediction is designed to reduce category proliferation.Although HPTAM is still inevitably sensible to noisy outliers in the presence of noise,main experimental results show that HPTAM can achieve a balance between representation error and approximation error,which ameliorates the overall generalization capabilities.The recent Polytope ARTMAP（PTAM） suggests that irregular polytopes are more flexible than the predefined category geometries to approximate the borders among the desired output predictions.However,category expansion and adjustment steps without statistical information make PTAM not robust to noise and category overlap.In order to push the learning problem towards Structural Risk Minimization（SRM）,this paper proposes Hierarchical Polytope ARTMAP （HPTAM） to use a hierarchical structure with different levels,which are determined by the complexity of regions incorporating the input pattern.Besides,overlapping of simplexes from the same desired prediction is designed to reduce category proliferation.Although HPTAM is still inevitably sensible to noisy outliers in the presence of noise,main experimental results show that HPTAM can achieve a balance between representation error and approximation error,which ameliorates the overall generalization capabilities.

关 键 词：structural risk minimization polytope ARTMAP hierarchical structure representation error approximation error 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]