[1]戚湧,胡俊,於东军.基于自组织映射与概率神经网络的增量式学习算法[J].南京理工大学学报(自然科学版),2013,37(01):1.
 Qi Yong,Hu Jun,Yu Dongjun.Incremental learning algorithm based on selforganizing map and probabilistic neural network[J].Journal of Nanjing University of Science and Technology,2013,37(01):1.
点击复制

基于自组织映射与概率神经网络的增量式学习算法
分享到:

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

卷:
37卷
期数:
2013年01期
页码:
1
栏目:
出版日期:
2013-02-28

文章信息/Info

Title:
Incremental learning algorithm based on selforganizing map and probabilistic neural network
作者:
戚湧12胡俊1於东军12
1.南京理工大学 计算机科学与工程学院,江苏 南京 210094;2.南京理工大学 常熟研究院,江苏 常熟 215513
Author(s):
Qi Yong12Hu Jun1Yu Dongjun12
1.School of Computer Science and Engineering,NUST,Nanjing 210094,China; 2.Changshu Institute,NUST,Changshu 215513,China
关键词:
自组织映射概率神经网络增量式学习机器学习
Keywords:
selforganizing mapprobabilistic neural networkincremental learningmachine learning
分类号:
TP391.41
摘要:
为解决传统学习算法不能有效利用新可用数据这一不足,提出一种基于自组织映射(SOM)和概率神经网络(PNN)的增量式学习算法——增量式模块化自组织映射概率神经网络(IMSOMPNN)。使用模块化SOM对每类训练数据进行学习,以训练后SOM的原型向量作为此类别的模式神经元来构建PNN。IMSOMPNN可以方便地实现对不同类型的新数据进行增量式学习,并且在进行增量学习时,不再需要利用到原始的训练数据,仅使用新的数据对已有模型进行局部调整;最后,IMSOMPNN还具有较强的抗噪能力。在UCI Landsat Satellite数据集上的实验验证了该文所述方法的有效性。
Abstract:
To solve the defects of the traditional learning algorithms that the new available data can not be effectively utilized,a new incremental learning method called incremental modular selforganizing map probabilistic neural network(IMSOMPNN),based on selforganizing map(SOM)and probabilistic neural network(PNN)is proposed.Samples of each class are used to train a modular SOM and the codebook vectors of the trained SOMs are used as pattern neurons for constructing PNN.The proposed IMSOMPNN possesses several advantages such as:(1)it can easily learn the knowledge buried in different types of new available data;(2)only the new available data are used to update the trained model,and the original data do not need;(3)the proposed IMSOMPNN has good performance even on noisy data.Experimental results on the UCI Landsat Satellite dataset demonstrate the effectiveness of the proposed method.

参考文献/References:

[1]He H B,Chen S,Li K,et al.Incremental learning from stream data[J].IEEE Transactions on Neural Networks,2011,22(12):1901-1914.
[2]Wang Z L,Jiang M,Hu Y H,et al.An incremental learning method based on probabilistic neural networks and adjustable fuzzy clustering for human activity recognition by using wearable sensors[J].IEEE Transactions on Information Technology in Biomedicine,2012,16(4):691-699.
[3]Huang D,Yi Z,Pu X R.A new incremental PCA algorithm with application to visual learning and recognition[J].Neural Processing Letters,2009,30(3):171-185
[4]Zhao H,Yuen P C.Incremental linear discriminant analysis for face recognition[J].IEEE Trans Syst Man Cybern B Cybern,2008,38(1):210-221.
[5]Kohonen T.The selforganizing map[J].Proceedings of IEEE,1990,78(1):1461-1480.
[6]Specht D F.Probabilistic neural networks and the polynomial Adaline as complementary techniques for classification[J].IEEE Transactions on Neural Networks,1990,1(1):111-121.
[7]Ma F,Wang W P,Tsang W W,et al.Probabilistic segmentation of volume data for visualization using SOMPNN classifier[A].Proceedings of the 1998 IEEE Symposium on Volume Visualization[C].North Carolina,United States:ACM,1998:121-130.
[8]Yu D J,Shen H B,Yang J Y.SOMPNN:An efficient nonparametric model for predicting transmembrane helices[J].Amino Acids,2012,42(6):2195-2205.
[9]於东军,谌贻华,于海瑛.融合SOM与WangMendel方法的模糊规则提取[J].南京理工大学学报,2011,36(6):759-763. Yu Dognjun,Chen Yihua,Yu Haiying.Fuzzy rule extraction by fusing SOM and WangMendel method[J].Journal of Nanjing University of Science and Technology,2011,36(6):759-763.

相似文献/References:

[1]於东军,谌贻华,于海瑛.融合自组织映射与Wang-Mendel 方法的 模糊规则提取[J].南京理工大学学报(自然科学版),2011,(06):759.
 YU Dong-jun,CHEN Yi-hua,YU Hai-ying.Fuzzy Rule Extraction by Fusing SOM and Wang-Mendel Method[J].Journal of Nanjing University of Science and Technology,2011,(01):759.

备注/Memo

备注/Memo:
基金项目:国家自然科学基金(61272419);江苏省自然科学基金(BK2011371);江苏省博士后科研资助计划(1201027C);江苏省产学研联合创新资金前瞻性联合研究项目(BY2012022);中国航天CALT创新基金项目(CALT201102)
作者简介:戚湧(1970-),男,博士,教授,主要研究方向:机器学习,智能网络,Email:qyong@njust.edu.cn。
更新日期/Last Update: 2013-02-15