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A novel sparse kernel density estimator is derived based on a regression approach, which selects a very small subset of significant kernels by means of the D-optimality experimental design criterion using an orthogonal forward selection procedure. The weights of the resulting sparse kernel model are calculated using the multiplicative nonnegative quadratic programming algorithm. The proposed method...
Support vector machines have been extensively used in machine learning because of its efficiency and its theoretical background. This paper focuses on nu-transductive support vector machines for classification (nu-TSVC) and construct a new algorithm - Unconstrained nu-Transductive Support Vector Machines (Unu-TSVM). After researching on the special construction of primal problem in nu-TSVM, we transform...
The paper presents the application of a single-class Support Vector Machine (SVM) for localization of the focus region at the epileptic seizure on the basis of EEG registration. The diagnostic features used in recognition are derived from the directed transfer function description, determined for different ranges of EEG signals. The results of the performed numerical experiments for the localization...
The document similarity measure is a key point in textual data processing. It is the main responsible of the performance of a processing system. Since a decade, kernels are used as similarity functions within inner-product based algorithms such as the SVM for NLP problems and especially for text categorization. In this paper, we present a semantic space constructed from latent concepts. The concepts...
In many practical applications besides a small generalization error also the interpretability of classification systems is of great importance. There is always a tradeoff among these two properties of classifiers. The similarity measure in the input space as defined by one of the most powerful classifiers, the Random Forest (RF) algorithm, is used in this paper as basis for the construction of Generalized...
A novel support vector machine (SVM) with weighted features is proposed. To assign appropriate weights for each feature, a mutual information (MI) based approach is presented. Although the calculation of feature weights may add an extra computational cost, the proposed method generally exhibits better generalization performance over the traditional SVM. The numerical studies on one synthetic and five...
In this paper, a new algorithm for Support Vector classification is described. It is shown how to use the parametric margin model with non-constant radius. This is useful in many cases, especially when the noise is heteroscedastic, that is, where it depends on x. Moreover, for a priori chosen v, the proposed new SV classification algorithm has advantage of using the parameter 0 les v les 1 on controlling...
Although supervised learning has been widely used to tackle problems of function approximation and regression estimation, prior knowledge fails to be incorporated into the data-driven approach because the form of input-output data pairs are not applied. To overcome this limitation, focusing on the fusion between rough fuzzy system and very rare samples of input-output pairs with noise, this paper...
This paper presents a novel support vector regression (SVR) network for financial time series prediction. The SVR network consists of two layers of SVR: transformation layer and prediction layer. The SVRs in the transformation layer forms a modular network; but distinguished with conventional modular networks, the partition of the SVR modular network is based on the output domain that has much smaller...
In this paper, we present an effective computational approach for learning patterns of brain activity from the fMRI data. The procedure involved correcting motion artifacts, spatial smoothing, removing low frequency drifts and applying multivariate linear and non-linear kernel methods. Two novel techniques are applied: one utilizes the cosine transform to remove low-frequency drifts over time and...
This paper proposes an improved nonlinear canonical correlation analysis algorithm named radial basis function canonical correlation analysis (RBFCCA) for multivariate chaotic time series analysis and prediction. This algorithm follows the key idea of kernel canonical correlation analysis (KCCA) method to make a nonlinear mapping of the original data sets firstly with a RBF network and a linear neural...
In this work we show that a metaheuristic, the variable neighborhood search (VNS), can be effectively used in order to improve the performance of the hardware-friendly version of the support vector machine (SVM). Our target is the implementation of the feed-forward phase of SVM on resource-limited hardware devices, such as field programmable gate arrays (FPGAs) and digital signal processors (DSPs)...
In this paper, we investigate the wellposedness of the kernel adaline. The kernel adaline finds the linear coefficients in a radial basis function network using deterministic gradient descent. We will show that the gradient descent provides an inherent regularization as long as the training is properly early-stopped. Along with other popular regularization techniques, this result is investigated in...
Clustering with constraints is an active area in machine learning and data mining. In this paper, a semi-supervised kernel-based fuzzy C-means algorithm called PCKFCM is proposed which incorporates both semi-supervised learning technique and the kernel method into traditional fuzzy clustering algorithm. The clustering is achieved by minimizing a carefully designed objective function. A kernel-based...
Conventional cost functions of adaptive filtering are usually related to the errorpsilas dispersion, such as errorpsilas moments or errorpsilas entropy, but neglect the shape aspects (peaks, kurtosis, tails, etc.) of the error distribution. In this work, we propose a new notion of filtering (or estimation) in which the errorpsilas probability density function (PDF) is shaped into a desired one. As...
The concept of linear perceptron or spherical perceptron in confomal geometry is extended to the more general conic perceptron, namely the elliptical perceptron. By means of the d-uple embedding a polynomial kernel of degree d is used, which is widely known in SVMpsilas for neural networks. By associating the Clifford algebra to the vector space of conics the conic separator is introduced, generalizing...
One of the main requirements of biometric systems is the ability of producing very low false acceptation rate, which very often can be achieved only by combining different biometric traits. The literature has shown that the pattern classification approach usually surpasses the classifier combination approach for this task. In this work we take into account the pattern classification approach, but...
Ranking support vector machine (RSVM) learning is equivalent to solving a convex quadratic programming problem. Currently there exists some difficulties for exact online ranking learning. This paper presents an exact and effective method that can solve the online ranking learning problem and shows the feasibility and finite convergence of the algorithm from the perspective of theoretical analysis...
Autonomous systems for surveillance, security, patrol, search and rescue are the focal point of extensive research and interest from defense and the security related industry, traffic control and other institutions. A range of sensors can be used to detect and track objects, but optical cameras or camcorders are often considered due to their convenience and passive nature. Tracking based on color...
Based on least squares wavelet support vector machines (LS-WSVM) with quantum-inspired evolutionary algorithm (QEA), the prediction model of urban passenger transport is proposed , that can provide the theoretical foundation of forecasting passenger volume of urban transport accurately. The prediction model of urban passenger transport is established by using LS-WSVM, whose regularization parameter...
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