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Semisupervised scheme has emerged as a popular strategy in the machine learning community due to the expensiveness of getting enough labeled data. In this paper, a semisupervised incremental support vector machine (SE-INC-SVM) algorithm based on neighborhood kernel estimation is proposed. First, kernel regression is constructed to estimate the unlabeled data from the labeled neighbors and its estimation...
We investigate general concept detection in unconstrained videos. A distance metric learning algorithm is developed to use the information of the group let structure for improved detection. A group let is defined as a set of audio and/or visual code words that are grouped together according to their strong correlations in videos. By using the entire group lets as building elements, concepts can be...
This paper describes a nested virtualization solution, which allows virtual machine monitor (VMM) with virtual machine to run within another virtual machine with low overhead. Previous nested virtualization solutions on x86 platform are mainly based on emulation, which result in poor performance and poor usability. We propose and implement NestCloud, a practical high performance nested virtualization...
Multi-core architecture provides more on-chip parallelism and powerful computational capability. It helps virtualization achieve scalable performance. KVM (kernel based virtual machine) is different from other virtualization solutions which can make use of the Linux kernel components such as completely fair scheduler (CFS). However, CFS treats the KVM threads as normal tasks without considering about...
An improved particle filter for nonlinear, non-Gaussian estimation is proposed in this paper. The algorithm consists of a particle filter that uses a proximal support vector regression (PSVR) based re-weighting scheme to re-approximate the posterior density and avoid sample impoverishment. A regression function is obtained by PSVR over the weighted sample set and each sample is re-weighted via this...
CPU scheduler is a very important subsystem which affects system throughput, interactivity and fairness. The development of Linux kernel is relatively fast-paced. By now, many CPU schedulers have been designed by researchers, hobbyists and kernel hackers. It is necessary to accurately compare and analyze different characteristics among these schedulers, so as to understand and design better CPU schedulers...
Nowadays, one of focus is the studies of the relationship between exposure to electromagnetic fields and human health. This paper proposed a predict model that evaluate the time workers in high voltage electric field (HVEF) long-term work in safety. With the economical improvements power industry is developing in high speed. More and more people are working in HVEF. In this paper, a method was proposed,...
Hardware acceleration is crucial in modern embedded system design to meet the explosive demands on performance and cost. Selected computation kernels for acceleration are usually captured by nest loops, which are optimized by state-of-the-art techniques like loop tiling and loop pipelining. However, memory bandwidth bottlenecks prevent designs to reach optimal throughput with respect to available...
In order to recognize stratums, a new support vector machine model (SVMM) is built on the basis of well-logging data and with RBF as its kernel function. Through the optimization of penalty parameter C and the introduction of a discriminant function, the classification accuracy of SVMM is greatly enhanced. Experiments show that the SVM classifier can be applied effectively to the recognition of stratums,...
In modern real-time embedded systems, to utilize the versatile services of a general kernel while without sacrificing the real-time performance is a great challenge. In the previous work, the services and the real-time kernel run in the same environment without sufficient protection mechanisms, which always leads to the system unreliability. We propose the jMoni architecture, which offers a novel...
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