Description: FASBIR(Filtered Attribute Subspace based Bagging with Injected Randomness) is a variant of Bagging algorithm, whose purpose is to improve accuracy of local learners, such as kNN, through multi-model perturbing ensemble.
Reference: Z.-H. Zhou and Y. Yu. Ensembling local learners through multimodal perturbation. IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics, 2005, vol.35, no.4, pp.725-735.
Description: C4.5Rule-PANE is a rule learning method which could generate accurate and comprehensible symbolic rules, through regarding a neural network ensemble as a pre-process of a rule inducer.
Reference: Z.-H. Zhou and Y. Jiang. Medical diagnosis with C4.5 rule preceded by artificial neural network ensemble. IEEE Transactions on Information Technology in Biomedicine, 2003, vol.7, no.1, pp.37-42.
使用神經(jīng)網(wǎng)絡(luò)集成方法診斷糖尿病,肝炎,乳腺癌癥的案例研究.
本文件為用C語言實現(xiàn)的可實現(xiàn)廣義異或問題的bp神經(jīng)網(wǎng)絡(luò)算法。該問題是對標(biāo)準(zhǔn)異或問題的推廣。在標(biāo)準(zhǔn)異或問題中，輸入X1和X2取離散量-1或+1，在廣義異或問題中，輸入（X1,X2）可以在區(qū)間[-1，+1] X [-1, +1]內(nèi)任意取值，而輸出為Y=sign(x1,x2)，其中sign()為符號函數(shù)，在區(qū)間[-1，+1] X [-1, +1]內(nèi)隨機(jī)產(chǎn)生500個訓(xùn)練樣本.本程序用標(biāo)準(zhǔn)BP網(wǎng)實現(xiàn)該分類問題.
