The paper presents a novel learning algorithm for the class of L2 Support Vector Machines classifiers dubbed Direct L2 SVM. The proposed algorithm avoids solving the quadratic programming problem and yet, it produces both the same exact results as the classic quadratic programming based solution in a significantly shorter CPU time. The connections between various L2 SVM algorithms will be highl...

Spirometry test is an inevitable, essential screening test in the case of respiratory and lung related diseases. This work focuses on predicting FEV1, which is the most significant and one of the deciding value in making the conclusion on respiratory related disorders by Least Squares Support Vector Machine (LS SVM) regression. This prediction of FEV1 values will enhance the spirometric method,...

| Mixture modelling is becoming an increasingly important tool in the remote sensing community as researchers attempt to resolve sub-pixel, area information. This paper compares a well-established technique, Linear Spectral Mixture Models (LSMM), with a much newer idea based on data selection, Support Vector Machines (SVM). It is shown that the constrained least squares LSMM is equivalent to th...

Predicting the deformation of landslides is significant for landslide early warning. Taking Shuping in Three Gorges Reservoir area (TGRA) as a case, displacement decomposed into two components by time series model (TSM). The least squares support vector machine (LSSVM) optimized particle swarm optimization (PSO) selected to predict prediction based on rainfall and reservoir water level (RWL). F...

The bankruptcies of companies have been predicted with numerous methods. In this paper, the ensemble of Locally Linear model is compared to Linear Discriminant Analysis, Least Squares Support Vector Machines and Optimally Pruned Extreme Learning Machines. To create the ensemble, diffrerent basis for the locally linear models as well as different combinations of variables are used in order to ob...

In this paper, a data-driven modeling technique is proposed for temperature forecasting. Due to the high dimensionality, LASSO is used as feature selection approach. Considering spatio-temporal structure of the weather dataset, first LASSO is applied in a spatial and temporal scenario, independently. Next, a feature is included in the model if it is selected by both. Finally, Least Squares Supp...

ISSn: 0967-0335 © IM publications llp 2010 doi: 10.1255/jnirs.883 all rights reserved the measurement of physical and chemical parameters of soil is an important step toward sustainable farming practices, landscaping management and, more generally, the understanding of terrestrial ecosystem processes. Standard soil analytical procedures are often complex, time-consuming, and expensive for many ...

Extreme Support Vector Machine (ESVM), a variant of ELM, is a nonlinear SVM algorithm based on regularized least squares optimization. In this chapter, a regression algorithm, Extreme Support Vector Regression (ESVR), is proposed based on ESVM. Experiments show that, ESVR has a better generalization ability than the traditional ELM.Furthermore, ESVMcan reach comparable accuracy as SVR and LS-SV...

a simple and rapid method for the determination of 137ba isotope abundances in water samples by inductively coupled plasma-optical emission spectrometry (icp-oes) coupled with least-squares support vector machine regression (ls-svm) is reported. by evaluation of emission lines of barium, it was found that the emission line at 493.408 nm provides the best results for the determination of 137ba a...