6644016
Multistar Decomposition of Complete Multigraphs
32008979
Isomorphic Path Decompositions of Crowns
14723095
Optimal Priority Packetization with Multi-layer UEP for Video Streaming over Wireless Network
Most of current packetization schemes consider only bit error or packet erasure, both of which are common in wireless networks. This paper addresses these two problems together, and proposes an optimal packetization scheme for video streaming over wireless network, which is independent of video coding method. To combat the packet erasure, priority packetization combined with multi-layer unequal error protection (UEP) is applied on video frames. Multi-layer UEP contains low-complexity duplication of high-priority packet in application layer and different retransmission limit in media access control layer. Content-aware rate-distortion optimization is also introduced in order to countermine the distortion caused by bit errors. Simulations show that our scheme gains 2.17 dB or more compared with the conventional scheme.
36777484
A Simple Decomposition Method for Support Vector Machines
The decomposition method is currently one of the major methods for solving support vector machines. An important issue of this method is the selection of working sets. In this paper through the design of decomposition methods for bound-constrained SVM formulations we demonstrate that the working set selection is not a trivial task. Then from the experimental analysis we propose a simple selection of the working set which leads to faster convergences for difficult cases. Numerical experiments on different types of problems are conducted to demonstrate the viability of the proposed method.
41121683
Decomposition Methods for Linear Support Vector Machines
In this letter, we show that decomposition methods with alpha seeding are extremely useful for solving a sequence of linear support vector machines (SVMs) with more data than attributes. This strategy is motivated by Keerthi and Lin (2003), who proved that for an SVM with data not linearly separable, after C is large enough, the dual solutions have the same free and bounded components. We explain why a direct use of decomposition methods for linear SVMs is sometimes very slow and then analyze why alpha seeding is much more effective for linear than nonlinear SVMs. We also conduct comparisons with other methods that are efficient for linear SVMs and demonstrate the effectiveness of alpha seeding techniques in model selection.
11225382
Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel
Support vector machines (SVMs) with the gaussian (RBF) kernel have been popular for practical use. Model selection in this class of SVMs involves two hyper parameters: the penalty parameter C and the kernel width . This letter analyzes the behavior of the SVM classifier when these hyper parameters take very small or very large values. Our results help in understanding the hyperparameter space that leads to an efficient heuristic method of searching for hyperparameter values with small generalization errors. The analysis also indicates that if complete model selection using the gaussian kernel has been conducted, there is no need to consider linear SVM.
9009444
A Portable Parallel N-Body Solver
We present parallel solutions for direct and fast n-body solvers written in the ZPL language. We describe the direct solver, compare its performance against a sequential C program, and show performance results for two very different parallel machines; the KSR-2 and the Paragon. We also discuss the implementation of the fast solver in ZPL, including factors pertinent to data movement.