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Intelligent control of autonomous vehicles has been an important research topic due to the model uncertainties and complexities of vehicle dynamics. In this paper, we proposed an approximate dynamic programming (ADP) approach for path following control of an autonomous vehicle. The idea is to use the Dual Heuristic Programming (DHP) algorithm, which is an efficient class of ADP methods, to directly...
Support vector machines (SVMs) have been dominant learning techniques for more than ten years, and mostly applied to supervised learning problems. These years two-class unsupervised and semi-supervised classification algorithms based on bounded C-SVMs, bounded j/-SVMs and Lagrangian SVMs (LSVMs) respectively, which are relaxed to semi-definite programming (SDP), get good classification results. These...
Support Vector Machines (SVMs) have been dominant learning techniques for more than ten years, and mostly applied to supervised learning problems. These years two-class unsupervised and semi-supervised classification algorithms based on Bounded C-SVMs, Bounded n-SVMs and Lagrangian SVMs (LSVMs) respectively, which are relaxed to Semi-definite Programming (SDP), get good classification results. These...
Support vector machines (SVMs) have been dominant learning techniques for more than ten years, and mostly applied to supervised learning problems. These years two-class unsupervised and semi-supervised classification algorithms based on bounded C-SVMs, bounded ??-SVMs and Lagrangian SVMs (LSVMs) respectively, which are relaxed to semi-definite programming (SDP), get good classification results. These...
Support vector machines (SVMs) have been dominant learning techniques for almost ten years, and mostly applied to supervised learning problems. Recently nice results are obtained by two-class unsupervised and semi-supervised classification algorithms where the optimization problems based on bounded C-SVMs, bounded v-SVMs and Lagrangian SVMs respectively are relaxed to semi-definite programming (SDP)...
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