Using a growing probabilistic neural network to reinforce a semi supervised support vector machine

Citations Over Time

Abstract

In this paper, we propose to reinforce the Self-Training strategy in a semi-supervised learning by using a Growing Probabilistic Neural Network (GPNN) which combines clustering and classification. The main advantages of this neural network are the linkage between data topology preservation and classes representation by using the cluster posterior probabilities of classes. It is a constructive model without prior conditions such as a suitable number of neurons. A new neuron is inserted when new data are not represented by existing neurons. For the Self-Training strategy, we chose the Support Vector Machines (SVM) as classifier because the SVMs are a powerful machine learning technique based on the principle of structural risk minimization. The proposed approach has been tested on synthetic and real datasets. Obtained results are very promising.

Related Papers

• GSVMR Model on Short-term Forecasting of City Road Traffic Volume(2008)
• → On Constructive Groups(1969)3 cited
• → SLMBSVMs: a structural-loss-minimization-based support vector machines approach(2003)1 cited
• The Apply Of Constructive Method In Mathematics(2004)
• → As a Psychotherapist, How Do I Recognize Constructive and Destructive Use of Power?(2022)