Least squares support vector machine (LSSVM) is a learning algorithm based on statistical theory. Itsadvantages include robustness and calculation simplicity, it has good performance in the data processingof small samples. The LSSVM model lacks sparsity unable to handle large-scale problem, this articleproposes an method mixture kernel sparse This reduces theinitial training set sub-dataset usi...

The least-squares support vector machine is a frequently used kernel method for non-linear regression and classification tasks. Here we discuss several approximation algorithms for the least-squares support vector machine classifier. The proposed methods are based on randomized block kernel matrices, and we show that they provide good accuracy and reliable scaling for multi-class classification...

This paper proposes the least-squares support vector machine (LS-SVM) as an intelligent method applied on absorption spectra for the simultaneous determination of paracetamol (PCT), caffeine (CAF) and ibuprofen (IB) in Novafen. The signal to noise ratio (S/N) increased. Also, In the LS - SVM model, Kernel parameter (σ2) and capacity factor (C) were optimized. Excellent prediction was shown usin...

As an important part of power system planning and the basis of economic operation of power systems, the main work of power load forecasting is to predict the time distribution and spatial distribution of future power loads. The accuracy of load forecasting will directly influence the reliability of the power system. In this paper, a novel short-term Empirical Mode Decomposition-Grey Relational ...

Aiming at the problem of the large training data set leading to amounts of calculation a sparse approximation algorithm of least squares support vector machine are proposed. Firstly using the thought of matrix block, convert the optimization problem of a Least Squares support vector machine into low order symmetric positive definite linear systems. Furthermore, use conjugate gradient algorithm ...

In this publication, we present a method to characterize twotrack audio recordings (bass and drum instruments) based on musical attributes. These attributes are modelled using different regression algorithms. All regression models are trained based on score-based audio features computed from given scores and human annotations of the attributes. We compare five regression model configurations th...

Hybrid linear analysis (HLA), partial least-squares (PLS) regression, and the linear least square support vector machine (LSSVM) were used to determinate the soluble solids content (SSC) of apple by Fourier transform near-infrared (FT-NIR) spectroscopy. The performance of these three linear regression methods was compared. Results showed that HLA could be used for the analysis of complex solid ...