Search Results - "SUYKENS, J. A. K"

Efficiently updating and tracking the dominant kernel principal components by Hoegaerts, L., De Lathauwer, L., Goethals, I., Suykens, J.A.K., Vandewalle, J., De Moor, B.

“…The dominant set of eigenvectors of the symmetrical kernel Gram matrix is used in many important kernel methods (like e.g. kernel Principal Component Analysis,…”
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Interior-Point Lagrangian Decomposition Method for Separable Convex Optimization by Necoara, I., Suykens, J. A. K.

“…In this paper, we propose a distributed algorithm for solving large-scale separable convex problems using Lagrangian dual decomposition and the interior-point…”
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Optimal control by least squares support vector machines by Suykens, J.A.K., Vandewalle, J., De Moor, B.

“…Support vector machines have been very successful in pattern recognition and function estimation problems. In this paper we introduce the use of least squares…”
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Improved performance on high-dimensional survival data by application of Survival-SVM by Van Belle, V., Pelckmans, K., Van Huffel, S., Suykens, J. A. K.

“…Motivation: New application areas of survival analysis as for example based on micro-array expression data call for novel tools able to handle high-dimensional…”
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Additive regularization trade-off : Fusion of training and validation levels in kernel methods by PELCKMANS, K, SUYKENS, J. A. K, DE MOOR, B

“…This paper presents a convex optimization perspective towards the task of tuning the regularization trade-off with validation and cross-validation criteria in…”
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Bayesian framework for least-squares support vector machine classifiers, gaussian processes, and kernel Fisher discriminant analysis by Van Gestel, T, Suykens, J A K, Lanckriet, G, Lambrechts, A, De Moor, B, Vandewalle, J

“…The Bayesian evidence framework has been successfully applied to the design of multilayer perceptrons (MLPs) in the work of MacKay. Nevertheless, the training…”
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Sequentially activated groups in neural networks by Komarov, M. A, Osipov, G. V, Suykens, J. A. K

“…The internal neuronal dynamics, network configurations and coupling are influencing the output of neuronal networks through different external actions. One…”
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Preoperative diagnosis of ovarian tumors using Bayesian kernel‐based methods by Van Calster, B., Timmerman, D., Lu, C., Suykens, J. A. K., Valentin, L., Van Holsbeke, C., Amant, F., Vergote, I., Van Huffel, S.

“…Objectives To develop flexible classifiers that predict malignancy in adnexal masses using a large database from nine centers. Methods The database consisted…”
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Influence of passive elements on the dynamics of oscillatory ensembles of cardiac cells by Petrov, V S, Osipov, G V, Suykens, J A K

“…In this paper we focus on the influence of passive elements on the collective dynamics of oscillatory ensembles. Two major effects considered are (i) the…”
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Metastable states and transient activity in ensembles of excitatory and inhibitory elements by Komarov, M. A, Osipov, G. V, Suykens, J. A. K

“…Complex activity in biological neuronal networks can be represented as a sequential transition between complicated metastable states. From a dynamical systems…”
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Application of Kernel Principal Component Analysis for Single-Lead-ECG-Derived Respiration by Widjaja, Devy, Varon, Carolina, Dorado, Alexander, Suykens, Johan A. K., Van Huffel, Sabine

“…Recent studies show that principal component analysis (PCA) of heartbeats is a well-performing method to derive a respiratory signal from ECGs. In this study,…”
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Reducing the Number of Support Vectors of SVM Classifiers Using the Smoothed Separable Case Approximation by Geebelen, D., Suykens, J. A. K., Vandewalle, J.

“…In this brief, we propose a new method to reduce the number of support vectors of support vector machine (SVM) classifiers. We formulate the approximation of…”
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A Convex Approach to Validation-Based Learning of the Regularization Constant by Pelckmans, K., Suykens, J.A.K., De Moor, B.

“…This letter investigates a tight convex relaxation to the problem of tuning the regularization constant with respect to a validation based criterion. A number…”
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Optimized fixed-size kernel models for large data sets by De Brabanter, K., De Brabanter, J., Suykens, J.A.K., De Moor, B.

“…A modified active subset selection method based on quadratic Rényi entropy and a fast cross-validation for fixed-size least squares support vector machines is…”
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Confidence bands for least squares support vector machine classifiers: A regression approach by De Brabanter, K., Karsmakers, P., De Brabanter, J., Suykens, J.A.K., De Moor, B.

“…This paper presents bias-corrected 100(1−α)% simultaneous confidence bands for least squares support vector machine classifiers based on a regression…”
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Knowledge discovery in a direct marketing case using least squares support vector machines by Viaene, S., Baesens, B., Van Gestel, T., Suykens, J. A. K., Van den Poel, D., Vanthienen, J., De Moor, B., Dedene, G.

“…We study the problem of repeat‐purchase modeling in a direct marketing setting using Belgian data. More specifically, we investigate the detection and…”
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Additive survival least-squares support vector machines by Van Belle, V., Pelckmans, K., Suykens, J. A. K., Van Huffel, S.

“…This work studies a new survival modeling technique based on least‐squares support vector machines. We propose the use of a least‐squares support vector…”
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Support vector machines versus logistic regression: improving prospective performance in clinical decision‐making by Pochet, N. L. M. M., Suykens, J. A. K.

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Handling missing values in support vector machine classifiers by Pelckmans, K., De Brabanter, J., Suykens, J.A.K., De Moor, B.

“…This paper discusses the task of learning a classifier from observed data containing missing values amongst the inputs which are missing completely at random 1…”
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Numerical studies of slow rhythms emergence in neural microcircuits: bifurcations and stability by Komarov, M A, Osipov, G V, Suykens, J A K, Rabinovich, M I

“…There is a growing body of evidence that slow brain rhythms are generated by simple inhibitory neural networks. Sequential switching of tonic spiking activity…”
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