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One of the key aspects in the successful use of kernel methods such as Support Vector Machines is the proper choice of the kernel function. While there are several well known kernel functions which can produce satisfactory results for various applications (e.g. RBF), they do not take into account specific characteristics of the data sets. Moreover, they have a set of parameters to be tuned. In this...
Regularization is used to find a solution that both fits the data and is sufficiently smooth, and thereby is very effective for designing and refining learning algorithms. But the influence of its exponent remains poorly understood. In particular, it is unclear how the exponent of the reproducing kernel Hilbert space (RKHS) regularization term affects the accuracy and the efficiency of kernel-based...
In various applications where the problem domain can be modeled into graphs, the shortest path computation in the graph is an indispensable challenge. In applications like online social networks and shortest route computation problems, the size of the graph is so large; the number of nodes have become close to hundreds of billions. Shortest path graph algorithms like SSSP (Single Source Shortest Path)...
Configuring an Evolutionary Algorithm (EA) can be a haphazard and inefficient process. An EA practitioner may have to choose between a plethora of search operator types and other parameter settings. In contrast, the goal of EA principled design is a more streamlined and systematic design methodology, which first seeks to better understand the problem domain, and only then uses such acquired insights...
Within the supervised machine learning framework, classifier performance is significantly affected by the size of training datasets. One of the ways to improve classification accuracy with small training datasets is to utilize additional knowledge about training data that is not present in testing data. In the Learning Using Privileged Information (LUPI) learning paradigm, this additional knowledge...
This paper analyzes and compares different Multiple Kernel Learning (MKL) algorithms for the classification of remote sensing (RS) images. The main purpose of the comparison is to identify advantages and disadvantages of different MKL algorithms in terms of their computational time and classification accuracy. Furthermore, some guidelines on the proper selection of the MKL algorithms associated with...
Fraud is a threat that most online service providers must address in the development of their systems to ensure an efficient security policy and the integrity of their revenue. If rule-based systems and supervised methods usually provide the best detection and prevention, labelled training datasets are often non-existent and such solutions lack reactivity when facing adaptive fraudsters. Many generic...
Many core systems are basically designed for applications having large data parallelism. Strassen Matrix Multiply (MM) can be formulated as a depth first (DFS) traversal of a recursion tree where all cores work in parallel on computing each of the NxN sub-matrices that reduces storage at the detriment of large data motion to gather and aggregate the results. We propose Strassen and Winograd algorithms...
We apply, in this article, a new method to identify outliers from a dataset. It consists to use the K-means clustering algorithm on the smallest principal components provided by the kernel principal components analysis. Two leading methods commonly used in the domain namely the standard deviation and the Tukey boxplot are tested and compared to our method. The experiments on artificial and real datasets...
This article presents an efficient hierarchical clustering algorithm that solves the problem of core community detection. It is a variant of the standard community detection problem in which we are particularly interested in the connected core of communities. To provide a solution to this problem, we question standard definitions on communities and provide alternatives. We propose a function called...
The identification of modules in complex networks is important for the understanding of systems. Recent studies have shown those functional modules can be identified from the protein interaction a network, what's more, the complex modules have not only relatively high density, but also have high coefficient of affinity. However, these analyses are challenging because of the presence of unreliable...
The past few years have witnessed debate on how to improve link utilization of high-speed tiny-size buffer routers. Widely argued proposals for TCP traffic to realize acceptable link capacities mandate: (i) over-provisioned core link bandwidth; and (ii) non-bursty flows; and (iii) tens of thousands of asynchronous flows. However, in high speed access networks where flows are bursty, sparse and synchronous,...
Aiming for the feature of low resolution and faint contrast for infrared image, a segmentation algorithm is presented based on the neighborhood weight fuzzy c-means kernel clustering. By using the Gaussian kernel in target function, the traditional euclidean distance in the FCM is replaced by a kernel-induced distance. At the same time, this method computes the sample weight during the clustering...
In this paper, we report on the development of an efficient GPU implementation of the Strassen-Winograd matrix multiplication algorithm for matrices of arbitrary sizes. We utilize multi-kernel streaming to exploit concurrency across sub-matrix operations in addition to intra-operation parallelism. We evaluate the performance of the implementation in comparison with CUBLAS-5.0 on Fermi and Kepler GPUs...
Collective communication operations in the Message Passing Interface (MPI) consume a significant amount of time at scale, degrading the performance of scientific applications. Optimizing collectives is key to application performance and scalability. This paper focuses on hiding the latency of the all gather collective by efficiently offloading it to the networking hardware. We have investigated the...
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