發(fā)布時間：2018-05-09 19:53

  本文選題：聚類 + 自組織映射(SOM)�。� 參考：《計算機工程與設計》2017年01期

【摘要】：研究貝葉斯正則化的自組織映射神經網絡(self-organizing map,SOM)聚類訓練算法。根據正則化的思想,在SOM權值調整公式中引入反映網絡權值復雜性的懲罰項,避免權值調整過程中出現過度擬合。利用貝葉斯推理獲取權值調整公式中的最優(yōu)超參數,使迭代訓練過程中網絡權值和輸入樣本的概率分布更趨于一致,達到提升SOM聚類結果的目的。在UCI數據集上的實驗結果表明,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】： 廣西大學計算機與電子信息學院;
【基金】：國家自然科學基金項目(61363027)
【分類號】：TP183;TP311.13

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本文編號：1867197