stage by gradually pruning the support value spectrum and optimizing the hyperparameters during the sparse approximation procedure. In this paper, twenty public domain benchmark datasets are used to evaluate the test set performance of LS-SVM classifiers with linear, polynomial and radial basis function

TreadMarks is a distributed shared memory (DSM) system for standard Unix systems such as SunOS and Ultrix. This paper presents a performance evaluation of TreadMarks running on Ultrix using DECstation-5000/240's that are connected by a 100-Mbps switch-based ATM LAN and a 10-Mbps Ethernet

... and detection, providing the vision and machine learning communities with a standard dataset of images and annotation, and standard evaluation procedures. Organised annually from 2005 to present, the challenge and its associated dataset has become accepted as the benchmark for object detection

Abstract The U-Net communication architecture provides processes with a virtual view of a network device to enable user-level access to high-speed communication devices. The architecture, implemented on standard workstations using off-the-shelf ATM communication hardware, removes the kernel from

Abstract—Dynamic Software Updating (DSU) has been an active topic of research for at least the last 30 years. However, despite many recent advances, DSU has yet to see widespread adoption and deployment in practice. In this paper, we review a slice of the history of DSU research to study how DSU for C has evolved over the last two decades. We examine the ways DSU systems are evaluated in the research literature. We identify several shortcomings of the evaluation criteria that have been used, and propose key improvements. We believe that using better evaluation criteria can guide DSU research to produce systems that will be more practical, flexible, and usable. I.

higher accuracy than currently available methods including TCoffee version 2 and CLUSTAL W in benchmark tests consisting of alignments of.50 sequences. Like the previously available options, the new options of MAFFT can handle hundreds of sequences on a standard desktop computer. We also examined

the standard algorithm by several orders of magnitude. In practice we obtain stereo, optical flow and image restoration algorithms that are as accurate as other global methods (e.g., using the Middlebury stereo benchmark) while being as fast as local techniques. 1

. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers -- the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows

We analyze critically the use of classification accuracy to compare classifiers on natural data sets, providing a thorough investigation using ROC analysis, standard machine learning algorithms, and standard benchmark data sets. The results raise serious concerns about the use of accuracy

, and it is an open problem to determine which of the approaches work better than others. Furthermore, larget-scale datasets for training machine learning based approaches are not available. DBpedia-NYD is a large-scale synthetic silver standard benchmark dataset which supports contains symmetric and asymmetric