Two sensitive wavelength (SW) selection methods combined with visible/near infrared (Vis/NIR) spectroscopy were investigated to determine the levels of some trace elements (Fe, Zn) in rice leaf. A total of 90 samples were prepared for the calibration (n = 70) and validation (n = 20) sets. Calibration models using SWs selected by LVA and ICA were developed and nonlinear regression of a least squ...

In this work, we develop and evaluate several least squares support vector machine (LS-SVM) classifiers within the Bayesian evidence framework, in order to preoperatively predict malignancy of ovarian tumors. The analysis includes exploratory data analysis, optimal input variable selection, parameter estimation, and performance evaluation via receiver operating characteristic (ROC) curve analys...

Sonomyography (SMG) is the signal we previously termed to describe muscle contraction using real-time muscle thickness changes extracted from ultrasound images. In this paper, we used least squares support vector machine (LS-SVM) and artificial neural networks (ANN) to predict dynamic wrist angles from SMG signals. Synchronized wrist angle and SMG signals from the extensor carpi radialis muscle...

The Bayesian evidence framework has been successfully applied to the design of multilayer perceptrons (MLPs) in the work of MacKay. Nevertheless, the training of MLPs suffers from drawbacks like the nonconvex optimization problem and the choice of the number of hidden units. In support vector machines (SVMs) for classification, as introduced by Vapnik, a nonlinear decision boundary is obtained ...

Four novel EEG signal features for discriminating phase-coded steady-state visual evoked potentials (SSVEPs) are presented, and their performance in view of target selection in an SSVEP-based brain-computer interfacing (BCI) is assessed. The novel features are based on phase estimation and correlations between target responses. The targets are decoded from the feature scores using the least squ...

Usually time series prediction is done with regularly sampled data. In practice, however, the data available may be irregularly sampled. In this case the conventional prediction methods cannot be used. One solution is to use Functional Data Analysis (FDA). In FDA an interpolating function is fitted to the data and the fitting coefficients are being analyzed instead of the original data points. ...

Abstract This paper proposes a method which using density index function to sparse LS-SVM in highdimensional feature space, and gives a new method which takes each sample point as a clustering center to make hypersphere, so as to determine the fuzzy membership function in high-dimensional feature space, thus to establish a new fuzzy least squares support vector machine model, So it is different...

In Functional Data Analysis (FDA) multivariate data are considered as sampled functions. We propose a non-supervised method for finding a good function basis that is built on the data set. The basis consists of a set of Gaussian kernels that are optimized for an accurate fitting. The proposed methodology is experimented with two spectrometric data sets. The obtained weights are further scaled u...

The precise prediction of coal seam thickness in operating mines is crucial for the construction transparent mines. Geological borehole data or a small amount seismic information frequently used traditional methods; however, these methods have poor precision. In this study, we introduced model predicting based on comprehensive preference attribute combination (CPSAC) and least squares support v...