A recurrent neural network-based method for training probabilistic Support Vector Machine

نویسنده:
, , , ,
سال
: 2009
چکیده: In this paper, Support Vector Machine (SVM) is reformulated to a recurrent neural

network model which can be described by the nonlinear dynamic system. In the proposed

algorithm, an iterative training procedure is proposed independent of initial point. Also

probabilistic constraints are recommended for reducing effect of noisy samples in training

procedure and appearance of incorrect Support Vectors (SV). Probabilistic constraints admit

using knowledge about distribution function of samples. A set of differential equations is used

to modelling of the proposed probabilistic SVM. These equations are converged to optimal

solution for SVM. The Euler method is used to solve differential equation. The primal and

dual problem of SVM is solved by this model. Enough information is given for finding

optimal hyper plane. Capability of the proposed method is shown by experimental results in the

Optical Character Recognition (OCR) and synthetic data.
کلیدواژه(گان): recurrent neural network model,differential equation,probabilistic constraints,SVC,

support vector machine,OCA,optical character recognition
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