|Table of Contents|

Incremental learning algorithm based on selforganizing map and probabilistic neural network

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

Issue:
2013年01期
Page:
1-
Research Field:
Publishing date:

Info

Title:
Incremental learning algorithm based on selforganizing map and probabilistic neural network
Author(s):
Qi Yong12Hu Jun1Yu Dongjun12
1.School of Computer Science and Engineering,NUST,Nanjing 210094,China; 2.Changshu Institute,NUST,Changshu 215513,China
Keywords:
selforganizing mapprobabilistic neural networkincremental learningmachine learning
PACS:
TP391.41
DOI:
-
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.

References:

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Last Update: 2013-02-15