基于Landmark技術(shù)的中國螳瘤蝽翅面特征提取與種群區(qū)分研究.pdf

1、中國農(nóng)業(yè)大學(xué)碩士學(xué)位論文基于Lmark技術(shù)的中國螳瘤蝽翅面特征提取與種群區(qū)分研究姓名：王權(quán)申請學(xué)位級別：碩士專業(yè)：應(yīng)用數(shù)學(xué)指導(dǎo)教師：陳薇20060601AbstractIntlIispaperWefoundaiflSCCtwingsurfacefeatumextractionmethodwhich／sinvirtueofprogramminginMATLABandmicrographyOurresearchisbasedontheLan

2、dmarktechniqueofthemorphometricsandundertheexperimentconditionandtheinsectsampleswehadThismethodmakesthefeatoreextractionofinsectstobesemiautomatic，intelligantizedanddigitalAtthesametimeweusetheclassificationalgorithmswh

3、ichwerederivedfrompatternrecognitionandmaltivariatestatisticalanalysisjninsectdassificationWeusedthetraditionalclustermethodsandal∞combinedtheextensiontheoryandtraditionalmethodsThispaperimprovedtheextensionclustermethod

4、andthelinearpmgrarnSVMWhichisbasedonminimizedthepesitivefactorsT五emainresearchworksofthis∞per：1WemadesomesummarizeaboutcurrentclassificationmethodsofinsectaespeciallythenumericalmethodsAtthesametimewesummedupthemeansofma

5、thwhichwereaDpearinthispapersuchasmorphometrics，clusteralgorithms，andthemachinelearningmeans，etc2ThispapercarriedouttheextractionoftheveincrnnodeLandmarkdataoftheCnizocorissinesissemi—automaticlyWeperformedtheanalysisoft

6、heinsectwingsurfacebasedoncomputerWesampledtheprocessformarIificiallysubjectiveanalysistomachinesemiautomatic3Wemodifiedandcarriedouttheextensionclusteralgorithmnemethodwasappliedtoourprobleminthispaperandgotideallyresul

7、tsomewhat4WiththealreadyexistedlinearprogramSVMwhichisbasedontheminimizedpositivefactors，weimportedtheextensionfactortoitSowegained也eextensionSVM啊lenewmethodimprovedtheextensiveabifityofSVMThemodifiedmethodgetgoodeffectw

8、iththedataofthispaperComparedwiththelinearFisherdiscriminancetheimprovedSVMmethodiSbetter’IhispapersuceessfullyappliedthemultivariatestatisticalanalysisandmachinelearningtheorytotheinsectidentificationespeciallytheHemipt