We proposed the support vector machine (SVM)-based equalisation schemes for direct-sequence ultra wideband (UWB) systems. The severe intersymbol interference caused by the UWB channel was formulated as a pattern classification problem in the SVM-based equaliser, which operates in two main modes: training and detection. We also applied the least squares support vector classifiers (LS-SVCs) to re...

Neural networks such as multilayer perceptrons and radial basis function networks have been very successful in a wide range of problems. In this paper we give a short introduction to some new developments related to support vector machines (SVM), a new class of kernelbased techniques introduced within statistical learning theory and structural risk minimization. This new approach leads to solvi...

Adaptive equalizers are used in digital communication system receivers to mitigate the effects of non-ideal channel characteristics and to obtain reliable data transmission. In this paper, we adopt least squares support vector machines (LS-SVM) for adaptive communication channel equalization. The LS-SVM involves equality instead of inequality constraints and works with a least squares cost func...

This paper explores the Support Vector Machine and Least Square Support Vector Machine models in stock forecasting. Three prevailing forecasting techniques General Autoregressive Conditional Heteroskedasticity (GARCH), Support Vector Regression (SVR) and Least Square Support Vector Machine (LSSVM) are combined with the wavelet kernel to form three novel algorithms Wavelet-based GARCH (WL_GARCH)...

Constitutive modeling of clay is an important research in geotechnical engineering. It is difficult to use precise mathematical expressions to approximate stress-strain relationship of clay. Artificial neural network (ANN) and support vector machine (SVM) have been successfully used in constitutive modeling of clay. However, generalization ability of ANN has some limitations, and application of...

Visible and near infrared (Vis/NIR) spectroscopy was investigated for the fast analysis of superoxide dismutase (SOD) activity in barley (Hordeum vulgare L.) leaves. Seven different spectra preprocessing methods were compared. Four regression methods were used for comparison of prediction performance, including partial least squares (PLS), multiple linear regression (MLR), least squares-support...

For fast detection of inorganic phosphorus fractions and their phosphorus contents in soil, a method employing near-infrared spectroscopy (NIRS) combined with partial least squares (PLS) and least squares support vector machine (LS-SVM) was proposed. Fifty soil samples for each of iron phosphate, magnesium phosphate, calcium phosphate and aluminum phosphate, with application rates of phosphate ...

Amino acids are quite important indices to indicate the growth status of oilseed rape under herbicide stress. Near infrared (NIR) spectroscopy combined with chemometrics was applied for fast determination of glutamic acid in oilseed rape leaves. The optimal spectral preprocessing method was obtained after comparing Savitzky-Golay smoothing, standard normal variate, multiplicative scatter corre...

In this paper we study four kernel machines for estimating expected shortfall, which are constructed through combinations of support vector quantile regression (SVQR), restricted SVQR (RSVQR), least squares support vector machine (LS-SVM) and support vector expectile regression (SVER). These kernel machines have obvious advantages such that they achieve nonlinear model but they do not require t...