The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Modern compilers use machine learning to find from their prior experience useful heuristics for new programs encountered in order to accelerate the optimization process. However, prior experience might not be applicable for outlier programs with unfamiliar code features. This paper presents a Reverse K-nearest neighbor (RKNN) algorithm based approach for outlier detection. The compiler can therefore...
In order to improve the generalization ability of feed-forward neural networks, a new objective function of learning procedure for training single hidden layer network is proposed. This objective function is composed of two information entropy, one is the cross entropy as the main optimization term and the other is the fuzzy entropy as the regularization term. In this paper, we are fused the concept...
Several object categorization algorithms use kernel methods over multiple cues, as they offer a principled approach to combine multiple cues, and to obtain state-of-the-art performance. A general drawback of these strategies is the high computational cost during training, that prevents their application to large-scale problems. They also do not provide theoretical guarantees on their convergence rate...
The Support Vector Machines (SVM) become popular E-Business data mining tools recently, and the datasets of E-Business are usually large-scale. If Support Vector Machines are trained on large-scale datasets, the training time will be very long and the classifier's accuracy will become lower too. As training a large-scale SVM is equated to solve a large-scale quadratic programming (QP) problem, so...
In this article is presented a method to design neural-genetic optimal controllers that are based on the fusion of a Recurrent Neural Network (RNN) and Genetic Algorithm (GA), these Computational Intelligence (CI) paradigms support the Linear Quadratic (LQR) design. The GA and RNN adaptation proprieties are the great advantage of the proposed approach, because all design is oriented to tune the optimal...
By solving a linear inverse problem under a sparsity constraint, one can successfully recover the coefficients, if there exists such a sparse approximation for the proposed class of signals. In this framework the dictionary can be adapted to a given set of signals using dictionary learning methods. The learned dictionary often does not have useful structures for a fast implementation, i.e. fast matrix-vector...
A recent work [1] proposed a novel group sparse classifier (GSC) that was based on the assumption that the training samples of a particular class approximately form a linear basis for any test sample belonging to that class. The group sparse classifier requires solving an NP hard group-sparsity promoting optimization problem. Thus a convex relaxation of the optimization problem was proposed. The convex...
We consider the problem of classification of multiple observations of the same object, possibly under different transformations. We view this problem as a special case of semi-supervised learning where all unlabelled examples belong to the same unknown class. We propose a low complexity solution that is able to exploit the properties of the data manifold with a graph-based algorithm. It results into...
We report our work on the algorithmic development of an evolutionary methodology for automatic configuration of metaheuristic algorithms for solving complex combinatorial optimization problems. We term it automatic configuration engine for metaheuristics (ACEM). We first propose a novel left variation-right property (LVRP) tree structure to manage various metaheuristic procedures and properties. With...
Support vector machine (SVM) has become a popular classification tool but one of its disadvantages is large memory requirement and computation time when dealing with large datasets. Parallel methods have been proposed to speed up the process of training SVM. An improved cascade SVM training algorithm is proposed, in which multiple SVM classifiers are applied. The support vectors are obtained by feeding...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.