The Experts below are selected from a list of 32568 Experts worldwide ranked by ideXlab platform
O. Henkel - One of the best experts on this subject based on the ideXlab platform.
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ICASSP (3) - Construction of coherent space time codes from non-coherent ones
Proceedings. (ICASSP '05). IEEE International Conference on Acoustics Speech and Signal Processing 2005., 1Co-Authors: O. HenkelAbstract:In this work, the natural Geometric Relation between the space time block code design if the channel is unknown at the receiver and its counterpart design if the receiver knows the channel is exploited, to establish an estimate on the corresponding diversities. This leads to a decomposition of code designs and splits the design problem into two complexity reduced sub tasks.
Baoliang Lu - One of the best experts on this subject based on the ideXlab platform.
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ISNN (1) - Task decomposition using Geometric Relation for min-max modular SVMs
Advances in Neural Networks — ISNN 2005, 2005Co-Authors: Kaian Wang, Hai Zhao, Baoliang LuAbstract:The min-max modular support vector machine (M3-SVM) was proposed for dealing with large-scale pattern classification problems. M3-SVM divides training data to several sub-sets, and combine them to a series of independent sub-problems, which can be learned in a parallel way. In this paper, we explore the use of the Geometric Relation among training data in task decomposition. The experimental results show that the proposed task decomposition method leads to faster training and better generalization accuracy than random task decomposition and traditional SVMs.
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task decomposition using Geometric Relation for min max modular svms
Lecture Notes in Computer Science, 2005Co-Authors: Kaian Wang, Hai Zhao, Baoliang LuAbstract:The min-max modular support vector machine (M 3 -SVM) was proposed for dealing with large-scale pattern classification problems. M 3 -SVM divides training data to several sub-sets, and combine them to a series of independent sub-problems, which can be learned in a parallel way. In this paper, we explore the use of the Geometric Relation among training data in task decomposition. The experimental results show that the proposed task decomposition method leads to faster training and better generalization accuracy than random task decomposition and traditional SVMs.
Kaian Wang - One of the best experts on this subject based on the ideXlab platform.
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ISNN (1) - Task decomposition using Geometric Relation for min-max modular SVMs
Advances in Neural Networks — ISNN 2005, 2005Co-Authors: Kaian Wang, Hai Zhao, Baoliang LuAbstract:The min-max modular support vector machine (M3-SVM) was proposed for dealing with large-scale pattern classification problems. M3-SVM divides training data to several sub-sets, and combine them to a series of independent sub-problems, which can be learned in a parallel way. In this paper, we explore the use of the Geometric Relation among training data in task decomposition. The experimental results show that the proposed task decomposition method leads to faster training and better generalization accuracy than random task decomposition and traditional SVMs.
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task decomposition using Geometric Relation for min max modular svms
Lecture Notes in Computer Science, 2005Co-Authors: Kaian Wang, Hai Zhao, Baoliang LuAbstract:The min-max modular support vector machine (M 3 -SVM) was proposed for dealing with large-scale pattern classification problems. M 3 -SVM divides training data to several sub-sets, and combine them to a series of independent sub-problems, which can be learned in a parallel way. In this paper, we explore the use of the Geometric Relation among training data in task decomposition. The experimental results show that the proposed task decomposition method leads to faster training and better generalization accuracy than random task decomposition and traditional SVMs.
Hai Zhao - One of the best experts on this subject based on the ideXlab platform.
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ISNN (1) - Task decomposition using Geometric Relation for min-max modular SVMs
Advances in Neural Networks — ISNN 2005, 2005Co-Authors: Kaian Wang, Hai Zhao, Baoliang LuAbstract:The min-max modular support vector machine (M3-SVM) was proposed for dealing with large-scale pattern classification problems. M3-SVM divides training data to several sub-sets, and combine them to a series of independent sub-problems, which can be learned in a parallel way. In this paper, we explore the use of the Geometric Relation among training data in task decomposition. The experimental results show that the proposed task decomposition method leads to faster training and better generalization accuracy than random task decomposition and traditional SVMs.
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task decomposition using Geometric Relation for min max modular svms
Lecture Notes in Computer Science, 2005Co-Authors: Kaian Wang, Hai Zhao, Baoliang LuAbstract:The min-max modular support vector machine (M 3 -SVM) was proposed for dealing with large-scale pattern classification problems. M 3 -SVM divides training data to several sub-sets, and combine them to a series of independent sub-problems, which can be learned in a parallel way. In this paper, we explore the use of the Geometric Relation among training data in task decomposition. The experimental results show that the proposed task decomposition method leads to faster training and better generalization accuracy than random task decomposition and traditional SVMs.
Hyunki Hong - One of the best experts on this subject based on the ideXlab platform.
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real time rendering method on spherical coordinate system using convex hull
대한전자공학회 기타 간행물, 2010Co-Authors: Namjung Kim, Hyunki HongAbstract:This paper presents a novel real-time rendering algorithm based on spherical coordinate system of the object using convex hull. While OpenGL rendering pipeline touches all vertices of an object, the proposed method takes account the only visible vertices by examining the visible triangles of the object. In order to determine the visible areas of the object in its spherical coordinate representation, the proposed method uses 3D Geometric Relation of 6 plane equations of the camera frustum and the bounding sphere. In addition, we compute the convex hull of the object and its maximum side factors for hidden surface removal. Simulation results showed that the proposed rendering algorithm achieve an efficient rendering performance.
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improved rendering on spherical coordinate system using convex hull
Journal of Korea Game Society, 2010Co-Authors: Namjung Kim, Hyunki HongAbstract:This paper presents a novel real-time rendering algorithm based on spherical coordinate system of the object using convex hull. While OpenGL rendering pipeline touches all vertices of an object, the proposed method takes account the only visible vertices by examining the visible triangles of the object. In order to determine the visible areas of the object in its spherical coordinate representation, the proposed method uses 3D Geometric Relation of 6 plane equations of the camera frustum and the bounding sphere of the object. In addition, we compute the convex hull of the object and its maximum side factors for hidden surface removal. Simulation results showed that the quality of result image is almost same compared to original image and rendering performance is greatly improved.