Abstract: Inordertoimprovetherecognitionrateofhandwrittendigitsbyneuralnetwork,theAdaboost-BPbinaryclassification algorithmisimprovedbasedontheideaofAdaboost,andtheAdaboost-BPalgorithmformulti-classificationisrealized,which improvestherecognitionrateofhandwrittendigitsbyneuralnetwork.Inthispaper,thecalculationformulaoftheweightvalueofthe "weak"classifierisimproved.Thestepofnormalizingtheweightvalueisputaftertheiterativetrainingofthe"weak"classifier,and thecompositionofthe"strong"classifieriscalculatedwithoutusingthesymbolfunctionbutdirectlycomputestheclassification results.TheexperimentaldataisbasedonMNISThandwrittendatabase.Theexperimentalresultsshowthatthecorrectrateofthe "strong"classifierconstructedbytheimprovedAdaboost-BPalgorithmisobviouslyhigherthanthatofthe"weak"classifier.The improvedAdaboost-BPalgorithmcanobviouslyimprovetheaccuracyofhandwrittendigitrecognition.

Key words: Adaboost-BPalgorithm, handwrittendigit, MNIST