本文選題：聚類 + 自組織映射(SOM)　； 參考：《計(jì)算機(jī)工程與設(shè)計(jì)》2017年01期

【摘要】：研究貝葉斯正則化的自組織映射神經(jīng)網(wǎng)絡(luò)(self-organizing map,SOM)聚類訓(xùn)練算法。根據(jù)正則化的思想,在SOM權(quán)值調(diào)整公式中引入反映網(wǎng)絡(luò)權(quán)值復(fù)雜性的懲罰項(xiàng),避免權(quán)值調(diào)整過程中出現(xiàn)過度擬合。利用貝葉斯推理獲取權(quán)值調(diào)整公式中的最優(yōu)超參數(shù),使迭代訓(xùn)練過程中網(wǎng)絡(luò)權(quán)值和輸入樣本的概率分布更趨于一致,達(dá)到提升SOM聚類結(jié)果的目的。在UCI數(shù)據(jù)集上的實(shí)驗(yàn)結(jié)果表明,與傳統(tǒng)的SOM算法相比,該算法的聚類凝聚度平均提升了1.5倍,聚類的準(zhǔn)確率亦有提高,聚類效果較好。
[Abstract]:A self-organizing map neural network clustering training algorithm for Bayesian regularization is studied. According to the idea of regularization, a penalty term reflecting the complexity of network weights is introduced into the SOM weight adjustment formula to avoid over-fitting in the course of weight adjustment. By using Bayesian reasoning to obtain the optimal super-parameters in the weight adjustment formula, the network weights and the probability distribution of input samples are more consistent in the iterative training process, and the purpose of improving the SOM clustering results is achieved. The experimental results on the UCI dataset show that compared with the traditional SOM algorithm, the clustering cohesion of the algorithm is 1.5 times higher, the accuracy of clustering is also improved, and the clustering effect is better.
【作者單位】： 廣西大學(xué)計(jì)算機(jī)與電子信息學(xué)院;
【基金】：國家自然科學(xué)基金項(xiàng)目(61363027)
【分類號】：TP183;TP311.13

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本文編號：1867197