1252619
A fast approximate algorithm for training L1-SVMs in primal space
We propose a novel and fast algorithm to train support vector machines (SVMs) in primal space, which solves an approximate optimization of SVMs with the properties of unconstraint, continuity and twice differentiability by utilizing the Newton optimization technique. Further, we devise a special pre-extracting procedure to speed up the convergence of the algorithm by resorting to a high-quality initial solution. Theoretical studies show that the proposed algorithm produces an @?-approximate solution to standard SVMs and maintains low computational complexity. Experimental results on benchmark data sets demonstrate that our algorithm is much faster than the dual based method such as SVM^l^i^g^h^t while it achieves the similar test accuracy.
36695959
Selective Ensemble Algorithms of Support Vector Machines Based on Constraint Projection
This paper proposes two novel ensemble algorithms for training support vector machines based on constraint projection technique and selective ensemble strategy. Firstly, projective matrices are determined upon randomly selected must-link and cannot-link constraint sets, with which original training samples are transformed into different representation spaces to train a group of base classifiers. Then, two selective ensemble techniques are used to learn the best weighting vector for combining them, namely genetic optimization and minimizing deviation errors respectively. Experiments on UCI datasets show that both proposed algorithms improve the generalization performance of support vector machines significantly, which are much better than classical ensemble algorithms, such as Bagging, Boosting, feature Bagging and LoBag.
734146
Using Description Logics Reasoner for Ontology Matching
Ontology matching produces correspondences between elements of two ontologies and it is a basic problem in many database application domains. A Description Logics-based ontology matching approach is proposed. The approach achieves the matching by means of semantic interpretation. The algorithm of deciding ontology correspondences (such as equivalence, more general, less general, disjointness, overlapping) is presented, which is reasoned based on concept inclusion. The reasoner RacerPro is selected to deduce ontology mapping, and the experimental results demonstrate that the approach is feasible.
15192522
A layered design methodology of cluster system stack
The application range of cluster has expanded beyond scientific computing, but the present cluster system software fails to provide a flexible architecture to promote code reuse and facilitate building cluster system software for different computing contexts, most of which are developed from scratch case by case, or integrated or packaged with ldquothe best practicerdquo. In this paper, we have proposed a layered design methodology to build cluster system stack with different layers concentrating on different functions, and developed common sets of core service as reusing framework for different computing context. Following this methodology, we have built Phoenix-a complete cluster system stack for both scientific and business computing, which is verified and deployed on Dawning 4000 A super computer for scientific computing and other cluster systems for business computing. The qualitative evaluation and our practices show the design methodology of Phoenix has advantages over other methodologies.
32146489
Cycle Period Analysis and Optimization of Timed Circuits
In this paper, a method is proposed to analyze the minimum average cycle period of the timed circuits. Timed Petri net is used to model timed circuits. Our method is focus on structural analysis of the Petri net model of the timed circuits, which is another way to reduce the state space of the analyzed model. Then an algorithm is proposed to optimize the performance of timed circuit by asynchronous retiming technique. The algorithm balances the asynchronous pipelines to gain the target cycle period while minimize the area at the same time. Experimental results demonstrate the computational feasibility and effectiveness of both approaches.
33446676
Voronoi Tessellation Based Rapid Coverage Decision Algorithm for Wireless Sensor Networks
The coverage problem is a fundamental issue in the research of sensor network. A novel Voronoi tessellation based rapid coverage decision algorithm is proposed, in which the region of interest is partitioned into some square grids firstly, and then the complex area coverage problem is transformed into simple vertices coverage problem, on the basis of the good characteristics of Voronoi tessellation such as being able to divide the planner area into a set of simple geometric simplexes. Theoretical analysis and simulation results show that, the new algorithm can decrease the computation complexity of networks, especially for sensor networks with n different sensors, it can solve the coverage problem correctly for any given region of interest with time costs of O(nlogn) only.
15142417
Low-Complexity Analysis of Repetitive Regularities for Biometric Applications
Presented in this paper is a joint algorithm optimization and architecture design framework for analysis of repetitive regularities. Two closely coupled algorithm optimization techniques, referred to as the prime subspace periodicity transform (PSPT) and circular periodicity transform (CPT), are developed that significantly reduce computational complexity while enable the extraction of a wide spectrum of periodic features. The proposed PSPTCPT algorithms lead to a parallel and resource sharing VLSI architecture. While most of the current systems rely on software solutions to performance feature extraction, the performance benefits rendered by the proposed framework show advantages in dealing with data-intensive computation for emerging applications in biometrics and bioinformatics. The explosive growth in database complexity combined with demand for fast analysis make the proposed framework a promising solution. Experimental results on an iris identification system demonstrate up to 99.2% accuracy and 24.6% − 41.9% reduction in computational complexity.