[PDF] Top 20 Sparse Deconvolution Using Support Vector Machines
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Sparse Deconvolution Using Support Vector Machines
... Significant knowledge about microphone arrays has been gained from years of intense research and product develop- ment. There have been numerous applications suggested, for example, from large arrays (on the order of ... See full document
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Support Vector Machines for Nonlinear Kernel ARMA System Identification
... where e c = " + C . Parameter controls the width of the L 2 interval between " and e c , so that the function is continuous and derivable, and the L 1 interval has slope C. The "-insensitivity zone provides ... See full document
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Support Vector Machines Framework for Linear Signal Processing
... a support vector machines (SVM) framework to deal with linear signal processing (LSP) ...(2) using a robust cost function, and (3) minimizing a constrained, regularized functional by means of ... See full document
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Growing Support Vector Classifiers with controlled complexity
... training using an oP-line cross-validation pro- ...initial machines working at a coarse level of detail), and progressively decreases this value in newly added kernels, such that "ner solutions are ... See full document
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Bootstrap Feature Selection in Support Vector Machines for Ventricular Fibrillation Detection
... In [10], a BR based method for feature selection is proposed, which is here briefly presented according to the principles in [9]. A dependence estimation process between pairs of data in a classification problem, where the ... See full document
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Support Vector Black-box Interpretation in Ventricular Arrhythmia Discrimination
... the support vector set is higher in SVTs with respect to SR, while the VTs scatter is still much greater than in the rest of ...in support vec- tor SVTs with respect to the support vec- tor SR ... See full document
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Time–Adaptive Support Vector Machines
... In this work we propose an adaptive classification method able both to learn and to follow the temporal evolution of a drifting concept. With that purpose we introduce a modified SVM classifier, created using ... See full document
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Support vector machines for explaining physiological stress response in Wood mice (Apodemus sylvaticus)
... In brief, our investigation points out that traditional statistical methods may not be enough to fully under- stand the causality among stressors and the stress response in wild mammals. This could be a common situation ... See full document
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ANALYSIS OF THE LAND USE AND COVER CHANGES IN THE METROPOLITAN AREA OF TEPIC-XALISCO (1973–2015) THROUGH LANDSAT IMAGES
... (MLC), Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs); from the preparation of the images (pre-processing) to the application of three supervised classification methods ... See full document
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Nonuniform Interpolation of Noisy Signals Using Support Vector Machines
... of support vectors SVs[%]) and the S/E obtained as a function of ...notably sparse solu- tion is ...more sparse than dual coefficients per ... See full document
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Robust Gamma-filter Using Support Vector Machines
... with t X N and s X 1; and denotes the time-local Pth order sample estimator of the autocorrelation function of the gamma-filtered versions of the input signal. Derivations of the dual functional in similar problems can be ... See full document
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TítuloTexture classification of proteins using support vector machines and bio inspired metaheuristics
... measurements using image segments from gel segmenta- ...by using shape information, since cracks and artifacts in gel surface deviate from a circular ... See full document
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TítuloHPF 2 Support for Dynamic Sparse Computations
... (Compressed Vector Storage), the first three schemes to represent sparse matrices, and the last one to represent sparse (one-dimensional) ...declared using the LLRCS storage scheme, as shown ... See full document
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Heart Rate Turbulence Denoising Using Support Vector Machines
... One of the key issues when using SVM algorithms is setting appropriate values for the free parameters. In this problem, where only 20 discrete-time samples are available, bootstrap resampling was used for this ... See full document
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Support Vector Method for Robust ARMA System Identification
... parameter vector; this approach includes the instrumental-variable 4 method (from now, Iv4), as well as several procedures for rational transfer function modeling ... See full document
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Support vector regression for tongue position inference
... Table (1) shows that exists some level of correlation between inputs and the residuals resulting from the regression ",#!..6( N4).( &-"?&$),*( ).( #,*C"?!+( J8( using the Brown-Forsythe test, ... See full document
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A support vector machine as an estimator of mountain papaya ripeness using resonant frequency or frequency centroid
... The firmness measurement methods used in this research are based on acoustic measurements of vibrational response of the fruit to impulsive me- chanical excitation. This is quite similar to the fine art of testing ... See full document
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Development of virtual reality machines to support training in automation
... LabVIEW using the necessary operations, conditions, structures, subVIs, validation and instructions that the VRM needs for having a behavior the closest to the real process as ...data, using event ... See full document
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Mtodo AGMSV (Algoritmos Genticos con Mquinas de Soporte Vectorial) con filtro mltiple para la reduccin de la dimensin y clasificacin de datos de micro arreglos de ADN
... by using a genetic algorithm (GA) combined with a support vector machine (SVM) for gene selection and classification of DNA microarray ...by using a GA/SVM framework using leave-one-out ... See full document
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Modelo basado en support vector machine para la estimación de la variabilidad de la frecuencia cardíaca
... Multiple combinations of inputs were applied for the algorithm training with the pur- pose of obtaining the best model for the HRV classification. Zhao et al., [21] describe a multiclass classification function in Matlab ... See full document
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