The Experts below are selected from a list of 4032 Experts worldwide ranked by ideXlab platform
Dahai Han - One of the best experts on this subject based on the ideXlab platform.
-
svm detection for superposed pulse amplitude modulation in visible light communications
Communication Systems Networks and Digital Signal Processing, 2016Co-Authors: Youli Yuan, Min Zhang, Pengfei Luo, Zabih Ghassemlooy, Danshi Wang, Xiongyan Tang, Dahai HanAbstract:A support vector machine (SVM)-based data detection for 8-superposed pulse amplitude modulation in visible light communication is proposed and experimentally demonstrated. In this work, the SVM detector contains three binary classifiers with different classification strategies. And the Separating Hyperplane of each SVM is constructed by training data. The experiment results show that the SVM detection offers 35% higher data rates when compared with the traditional direct decision method.
-
CSNDSP - SVM detection for superposed pulse amplitude modulation in visible light communications
2016 10th International Symposium on Communication Systems Networks and Digital Signal Processing (CSNDSP), 2016Co-Authors: Youli Yuan, Min Zhang, Pengfei Luo, Zabih Ghassemlooy, Danshi Wang, Xiongyan Tang, Dahai HanAbstract:A support vector machine (SVM)-based data detection for 8-superposed pulse amplitude modulation in visible light communication is proposed and experimentally demonstrated. In this work, the SVM detector contains three binary classifiers with different classification strategies. And the Separating Hyperplane of each SVM is constructed by training data. The experiment results show that the SVM detection offers 35% higher data rates when compared with the traditional direct decision method.
Jinghao Xue - One of the best experts on this subject based on the ideXlab platform.
-
a novel Separating Hyperplane classification framework to unify nearest class model methods for high dimensional data
IEEE Transactions on Neural Networks, 2020Co-Authors: Rui Zhu, Ziyu Wang, Naoya Sogi, Kazuhiro Fukui, Jinghao XueAbstract:In this article, we establish a novel Separating Hyperplane classification (SHC) framework to unify three nearest-class-model methods for high-dimensional data: the nearest subspace method (NSM), the nearest convex hull method (NCHM), and the nearest convex cone method (NCCM). Nearest-class-model methods are an important paradigm for the classification of high-dimensional data. We first introduce the three nearest-class-model methods and then conduct dual analysis for theoretically investigating them, to understand deeply their underlying classification mechanisms. A new theorem for the dual analysis of NCCM is proposed in this article by discovering the relationship between a convex cone and its polar cone. We then establish the new SHC framework to unify the nearest-class-model methods based on the theoretical results. One important application of this new SHC framework is to help explain empirical classification results: why one class model has a better performance than others on certain data sets. Finally, we propose a new nearest-class-model method, the soft NCCM, under the novel SHC framework to solve the overlapping class model problem. For illustrative purposes, we empirically demonstrate the significance of our SHC framework and the soft NCCM through two types of typical real-world high-dimensional data: the spectroscopic data and the face image data.
Ping Guo - One of the best experts on this subject based on the ideXlab platform.
-
an improved Separating Hyperplane method with application to embedded intelligent devices
International Conference on Neural Information Processing, 2014Co-Authors: Ping Guo, Xin XinAbstract:Classification is a common task in pattern recognition. Classifiers used in embedded intelligent devices need a good trade-off between prediction accuracy, resource consumption and prediction speed. Support vector machine(SVM) is accurate but its run-time complexity is higher due to the large number of support vectors. A new Separating Hyperplane method (NSHM) for the binary classification task was proposed. NSHM allows fast classification. However, NSHM is order-sensitive and this affects its classification accuracy. Inspired by NSHM, we propose CSHM, a combining Separating Hyperplane method. CSHM combines all optimal Separating Hyperplanes found by NSHM. Experimental results on UCI Machine Learning Repository show that, compared with NSHM and SVM, CSHM achieves a better trade-off between prediction accuracy, resource consumption and prediction speed.
John P Castagna - One of the best experts on this subject based on the ideXlab platform.
-
support vector machine svm pattern recognition to avo classification
Geophysical Research Letters, 2004Co-Authors: John P CastagnaAbstract:[1] The purpose of this paper is to present a learning algorithm to classify data with nonlinear characteristics. The Support Vector Machine (SVM) is a novel type of learning machine based on statistical learning theory [Vapnik, 1998]. The support vector machine (SVM) implements the following idea: It maps the input vector X into a high-dimensional feature space Z through some nonlinear mapping, chosen a priori. In this space, an optimal Separating Hyperplane is constructed to separate data groupings. The support vector machine (SVM) learning method can be used to classify seismic data patterns for exploration and reservoir characterization applications. The SVM is particularly good at classifying data with nonlinear characteristics. As an example the SVM method is applied to AVO classification of gas sand and wet sand.
Youli Yuan - One of the best experts on this subject based on the ideXlab platform.
-
svm detection for superposed pulse amplitude modulation in visible light communications
Communication Systems Networks and Digital Signal Processing, 2016Co-Authors: Youli Yuan, Min Zhang, Pengfei Luo, Zabih Ghassemlooy, Danshi Wang, Xiongyan Tang, Dahai HanAbstract:A support vector machine (SVM)-based data detection for 8-superposed pulse amplitude modulation in visible light communication is proposed and experimentally demonstrated. In this work, the SVM detector contains three binary classifiers with different classification strategies. And the Separating Hyperplane of each SVM is constructed by training data. The experiment results show that the SVM detection offers 35% higher data rates when compared with the traditional direct decision method.
-
CSNDSP - SVM detection for superposed pulse amplitude modulation in visible light communications
2016 10th International Symposium on Communication Systems Networks and Digital Signal Processing (CSNDSP), 2016Co-Authors: Youli Yuan, Min Zhang, Pengfei Luo, Zabih Ghassemlooy, Danshi Wang, Xiongyan Tang, Dahai HanAbstract:A support vector machine (SVM)-based data detection for 8-superposed pulse amplitude modulation in visible light communication is proposed and experimentally demonstrated. In this work, the SVM detector contains three binary classifiers with different classification strategies. And the Separating Hyperplane of each SVM is constructed by training data. The experiment results show that the SVM detection offers 35% higher data rates when compared with the traditional direct decision method.
