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...

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...

This paper contrasts three related regularization schemes for kernel machines using a least squares criterion, namely Tikhonov and Ivanov regularization and Morozov’s discrepancy principle. We derive the conditions for optimality in a least squares support vector machine context (LS-SVMs) where they differ in the role of the regularization parameter. In particular, the Ivanov and Morozov scheme...

For improving the precision of load forecasting in different time spans, a new model which combines improved complete ensemble empirical mode decomposition algorithm based on adaptive noise (ICEEMDAN) algorithm, least squares support vector machine (LS-SVM) and long short-term memory network (LSTM) is proposed. In this paper, training set acquired from department dispatch center large city nort...

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...

Here we propose a novel machine learning method for time series forecasting which is based on the widelyused Least Squares Support Vector Machine (LS-SVM) approach. The objective function of our method contains a weighted variance minimization part as well. This modification makes the method more efficient in time series forecasting as this paper will show. The proposed method is a generalizati...

In view of the remarkable time–frequency property obtained fromwavelet packets and the excellent generalization ability derived from the least squares support vector machine (LS SVM), a novel approach is proposed, which focuses on the research on state detection for pressure vessels. The minimum entropy criterion is adopted to realize the optimal wavelet packet decomposition, the feature vector...