Title:
Character-Recognition(Lib-SVM)
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Description: Support Vector Machine (SVM) is a new machine learning technique in recent years developed based on statistical learning theory (SLT). It wins popularity due to many attractive features and emphatically performance in the fields of nonlinear and high dimensional pattern recognition. The theory and algorithm of SVC is studied at first, then, simulation is to recognize handwritten numeral with the Lib-SVM toolbox. At last, we study the result, which shows that the SVC can do the classification problem with good performance, shorter operation time and is more suitable for real-time implementation.
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机器学习\102081504_任修齐.doc
........\2自编程序\CharacterRecognitionLibsvm.m
........\3算法工具\libsvm-mat-2.89-3[FarutoUltimate3.0]\COPYRIGHT
........\.........\....................................\heart_scale.mat
........\.........\....................................\implement[by faruto]\a_template_flow_usingSVM_class.m
........\.........\....................................\....................\a_template_flow_usingSVM_regress.m
........\.........\....................................\....................\fasticaForSVM.m
........\.........\....................................\....................\gaSVMcgForClass.m
........\.........\....................................\....................\gaSVMcgForRegress.m
........\.........\....................................\....................\gaSVMcgpForRegress.m
........\.........\....................................\....................\myprivate\gatbx[Sheffield]\bs2rv.m
........\.........\....................................\....................\.........\................\contents.m
........\.........\....................................\....................\.........\................\crtbase.m
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........\.........\....................................\....................\.........\................\mutate.m
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........\.........\....................................\....................\.........\................\reins.m
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........\.........\....................................\....................\.........\................\scaling.m
........\.........\....................................\....................\.........\................\select.m
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