Search Results - "Suykens, Johan A. K."

Support Vector Machine Classifier With Pinball Loss by Xiaolin Huang, Lei Shi, Suykens, Johan A. K.

“…Traditionally, the hinge loss is used to construct support vector machine (SVM) classifiers. The hinge loss is related to the shortest distance between sets…”
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Explaining Support Vector Machines: A Color Based Nomogram by Van Belle, Vanya, Van Calster, Ben, Van Huffel, Sabine, Suykens, Johan A K, Lisboa, Paulo

“…Support vector machines (SVMs) are very popular tools for classification, regression and other problems. Due to the large choice of kernels they can be applied…”
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Learning with tensors: a framework based on convex optimization and spectral regularization by Signoretto, Marco, Tran Dinh, Quoc, De Lathauwer, Lieven, Suykens, Johan A. K.

“…We present a framework based on convex optimization and spectral regularization to perform learning when feature observations are multidimensional arrays…”
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Multiway Spectral Clustering with Out-of-Sample Extensions through Weighted Kernel PCA by Alzate, C., Suykens, J.A.K.

“…A new formulation for multiway spectral clustering is proposed. This method corresponds to a weighted kernel principal component analysis (PCA) approach based…”
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A tutorial on support vector machine-based methods for classification problems in chemometrics by Luts, Jan, Ojeda, Fabian, Van de Plas, Raf, De Moor, Bart, Van Huffel, Sabine, Suykens, Johan A.K.

“…This tutorial provides a concise overview of support vector machines and different closely related techniques for pattern classification. The tutorial starts…”
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Coupled Simulated Annealing by Xavier-de-Souza, S., Suykens, J.A.K., Vandewalle, J., Bolle, D.

“…We present a new class of methods for the global optimization of continuous variables based on simulated annealing (SA). The coupled SA (CSA) class is…”
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Functional form estimation using oblique projection matrices for LS-SVM regression models by Caicedo, Alexander, Varon, Carolina, Van Huffel, Sabine, Suykens, Johan A K

“…Kernel regression models have been used as non-parametric methods for fitting experimental data. However, due to their non-parametric nature, they belong to…”
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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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Approximate Solutions to Ordinary Differential Equations Using Least Squares Support Vector Machines by Mehrkanoon, S., Falck, T., Suykens, J. A. K.

“…In this paper, a new approach based on least squares support vector machines (LS-SVMs) is proposed for solving linear and nonlinear ordinary differential…”
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Systematic benchmarking of microarray data classification: assessing the role of non-linearity and dimensionality reduction by Pochet, Nathalie, De Smet, Frank, Suykens, Johan A. K., De Moor, Bart L. R.

“…Motivation: Microarrays are capable of determining the expression levels of thousands of genes simultaneously. In combination with classification methods, this…”
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Netgram: Visualizing Communities in Evolving Networks by Mall, Raghvendra, Langone, Rocco, Suykens, Johan A K

“…Real-world complex networks are dynamic in nature and change over time. The change is usually observed in the interactions within the network over time…”
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Hinging Hyperplanes for Time-Series Segmentation by Xiaolin Huang, Matijas, Marin, Suykens, Johan A. K.

“…Division of a time series into segments is a common technique for time-series processing, and is known as segmentation. Segmentation is traditionally done by…”
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Parallelized Tensor Train Learning of Polynomial Classifiers by Chen, Zhongming, Batselier, Kim, Suykens, Johan A. K., Wong, Ngai

“…In pattern classification, polynomial classifiers are well-studied methods as they are capable of generating complex decision surfaces. Unfortunately, the use…”
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A mathematical model for interpretable clinical decision support with applications in gynecology by Van Belle, Vanya M C A, Van Calster, Ben, Timmerman, Dirk, Bourne, Tom, Bottomley, Cecilia, Valentin, Lil, Neven, Patrick, Van Huffel, Sabine, Suykens, Johan A K, Boyd, Stephen

“…Over time, methods for the development of clinical decision support (CDS) systems have evolved from interpretable and easy-to-use scoring systems to very…”
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Concurrent monitoring of operating condition deviations and process dynamics anomalies with slow feature analysis by Shang, Chao, Yang, Fan, Gao, Xinqing, Huang, Xiaolin, Suykens, Johan A.K., Huang, Dexian

“…Latent variable (LV) models have been widely used in multivariate statistical process monitoring. However, whatever deviation from nominal operating condition…”
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Low rank updated LS-SVM classifiers for fast variable selection by Ojeda, Fabian, Suykens, Johan A.K., De Moor, Bart

“…Least squares support vector machine (LS-SVM) classifiers are a class of kernel methods whose solution follows from a set of linear equations. In this work we…”
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Optimized Data Fusion for Kernel k-Means Clustering by Shi Yu, Tranchevent, Leon-Charles, Xinhai Liu, Glanzel, W., Suykens, J. A. K., De Moor, B., Moreau, Y.

“…This paper presents a novel optimized kernel k-means algorithm (OKKC) to combine multiple data sources for clustering analysis. The algorithm uses an…”
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Optimized data fusion for K-means Laplacian clustering by Yu, Shi, Liu, Xinhai, Tranchevent, Léon-Charles, Glänzel, Wolfgang, Suykens, Johan A. K., De Moor, Bart, Moreau, Yves

“…Motivation: We propose a novel algorithm to combine multiple kernels and Laplacians for clustering analysis. The new algorithm is formulated on a Rayleigh…”
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Robust Low-Rank Tensor Recovery With Regularized Redescending M-Estimator by Yuning Yang, Yunlong Feng, Suykens, Johan A. K.

“…This paper addresses the robust low-rank tensor recovery problems. Tensor recovery aims at reconstructing a low-rank tensor from some linear measurements,…”
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