A non-biased form of least squares support vector classifier and its fast online learning

As an effective learning technique based on structural risk minimization, SVM has been confirmed an useful tool in many machine learning fields. With the increase in application requirement for some real-time cases, such as fast prediction and pattern recognition, the online learning based on SVM gradually becomes a focus. But the common SVM has disadvantages in classifier’s bias and the computational complexity of online modeling, resulting in the reduction in classifier’s generality and the low learning speed. Therefore, an non-biased least square support vector classifier(LSSVC) model is proposed in this paper by improving the form of structure risk. Also, a fast online learning algorithm using Cholesky factorization is designed based on this model according to the characteristic of the non-biased kernel extended matrix in the model’s dynamic change process. In this way, the calculation of Lagrange multipliers is simplified, and the time of online learning is greatly reduced. Simulation results testify that the non-biased LSSVC has good universal applicability and better generalization capability, at the same time, the algorithm has a great improvement on learning speed.