The Relevance Vector Machine (RVM) algorithm has been widely utilized in many applications, such as machine learning, image pattern recognition, and compressed sensing. However, the RVM algorithm is computationally expensive. We seek to accelerate the RVM algorithm computation for time sensitive applications by utilizing massively parallel accelerators such as GPUs. In this paper, the computati...

Sensors networks instrument the physical space using motes that run network embedded programs thus acquiring, processing, storing and transmitting sensor data. The motes commercially available today are large, costly and trade performance for flexibility and ease of programming. New generations of motes are promising to deliver significant improvements in terms of power consumption and price — ...

The support vector machine (SVM) algorithm is well known to the computer learning community for its very good practical results. The goal of the present paper is to study this algorithm from a statistical perspective, using tools of concentration theory and empirical processes. Our main result builds on the observation made by other authors that the SVM can be viewed as a statistical regulariza...

As the speed of microprocessors increases at a breath-taking rate, the gap between processor and memory system performance is getting worse. To alleviate this problem, all modern processors contain caches, but even using caches, processors cannot achieve their peak performance. We propose a mechanism, smart caching, which extends the power of conventional memory subsystems by including a prefet...

Sparse matrix-vector multiplication (shortly SpM×V) is an important building block in algorithms solving sparse systems of linear equations, e.g., FEM. Due to matrix sparsity, the memory access patterns are irregular and utilization of the cache can suffer from low spatial or temporal locality. Approaches to improve the performance of SpM×V are based on matrix reordering and register blocking [...

This paper compares the performance of a recent version of the OCCAM Ocean Model and a more vector oriented version on two architectures, a parallel cache based SMP (an SGI Challenge) and a parallel vector based machine (a Fujitsu VPP 700). OCCAM was originally developed to run eeciently on cache based parallel platforms, with little attention paid to its vector performance. However, with the c...

As a major concern in designing various data mining applications, privacy preservation has become a critical component seeking a trade-off between mining performances and protecting sensitive information. Data perturbation or distortion is a widely used approach for privacy protection. Many privacy preservation approaches were developed, either by adding noises or by matrix decomposition method...

Fingerprint recognition is one of the most important Biometric techniques among all biometrics. It provides reliable means of biometric authentication due to its features Universality, Distinctiveness, Permanence and Accuracy. It is the method of identifying an individual and it can be used in various application, such as, medical records, criminal investigation, cloud computing communication e...