Probabilistic Inference-Based Least Squares Support Vector Machine for Modeling Under Noisy Environment

The least squares support vector machine (LS-SVM) has emerged as a popular data-driven modeling method and been extensively studied in the machine learning community. However, the LS-SVM is sensitive to noisy data and may not be effective when the level of noise is high. In this paper, a probabilistic LS-SVM is proposed to have a more reliable performance. First, a distributed LS-SVM is constructed with parameters estimated from data samples. Due to distributed nature of multiple LS-SVM, the stochastic property of parameters can be easily obtained and processed. Using the distribution characteristics of these parameters, the final outcome is derived through the probabilistic inference and thus be evaluated statistically. Its parallel structure is also suitable for parallel computing to reduce computing time. Both simulations and experiments demonstrate the effectiveness of the proposed probabilistic LS-SVM.