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Support vector machines (SVM)

Review of data mining applications for quality assessment in manufacturing industry: Support Vector Machines

Review of data mining applications for quality assessment in manufacturing industry: Support Vector Machines

... weighted support vector machines (WSVM) for automated process monitoring and early fault diagnosis, while original SVM demonstrate poor performance when applied directly to these ...

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Support vector machines: a tool for pattern recognition and classification

Support vector machines: a tool for pattern recognition and classification

... how machines can observe the environment, learn to distinguish pattern of interest from their background and make sound and reasonable decisions about the category of the ...named support vector ...

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Comparing support vector machines with logistic regression for calibrating cellular automata land use change models

Comparing support vector machines with logistic regression for calibrating cellular automata land use change models

... β. Support vector machines Along with artificial neural networks and genetic programming, SVM algorithms represent a new gen- eration of machine learning ...

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A Large Dimensional Analysis of Least Squares Support Vector Machines

A Large Dimensional Analysis of Least Squares Support Vector Machines

... squares support vector machines (LS-SVMs) are a modification of the standard SVM introduced in [2] to overcome the drawbacks of SVM related to computational ...

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The pharmacophore kernel for virtual screening with support vector machines

The pharmacophore kernel for virtual screening with support vector machines

... with support vector machines ...a vector or bitstring representation of molecules, they can also take advantage of a mathematical trick to only rely on a measure of similarity between ...

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Automatic pathology classification using a single feature machine learning - support vector machines

Automatic pathology classification using a single feature machine learning - support vector machines

... using Support Vector Machines (SVM) 1 and easy to obtain geometrical measurements that, together with a cortical and sub-cortical brain parcellation, create a robust framework capable of automatic ...

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Combining gradient ascent search and support vector machines for effective autofocus of a field emission–scanning electron microscope

Combining gradient ascent search and support vector machines for effective autofocus of a field emission–scanning electron microscope

... This paper investigates autofocus of the SEM, precisely a Zeiss Auriga FE-SEM. The developed solution combines the advantage of coarse-to-fine search, that is speed, with those of machine learning fitting search ...

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Least-squares support vector machines modelization for time-resolved spectroscopy

Least-squares support vector machines modelization for time-resolved spectroscopy

... numerical optimization 11,12 , analytical decriptor of temporal dispersion 13 . Since the signal can not be described by a linear equation (this is why curves descriptors are often used), a non linear multivariate model ...

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Using Support Vector Machines to identify determinants of pronoun difficulty in aphasia: a preliminary critical review and meta-analysis of individual data

Using Support Vector Machines to identify determinants of pronoun difficulty in aphasia: a preliminary critical review and meta-analysis of individual data

... the Support Vector Machines regression model (SVM; Scholkopf & Smola, 2001) as this machine learning algorithm is well-suited for continuous data with large number of predicting variables and is ...

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Kernel functions for molecular structures and their application to virtual screening with Support Vector Machines

Kernel functions for molecular structures and their application to virtual screening with Support Vector Machines

... with support vector machines ...a vector or bitstring representation of molecules, they can also take advan- tage of a mathematical trick to only rely on a measure of similarity between ...

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Decoding Semi-Constrained Brain Activity from fMRI Using Support Vector Machines and Gaussian Processes

Decoding Semi-Constrained Brain Activity from fMRI Using Support Vector Machines and Gaussian Processes

... (Support Vector Machines, SVM, and Gaussian Processes, GP), as well as different feature extraction methods (General Linear Model, GLM and ...

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Epitope prediction improved by multitask support vector machines

Epitope prediction improved by multitask support vector machines

... Identifying MHC class I epitope in a pathogen genome is therefore crucial for vac- cine design. However, not all peptides of a pathogen can bind to the MHC molecule to be presented to T-cells: it is estimated that only 1 ...

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Efficient brain lesion segmentation using multi-modality tissue-based feature selection and support vector machines

Efficient brain lesion segmentation using multi-modality tissue-based feature selection and support vector machines

... Support Vector Machines (SVM) are a machine learning technique that have been used for segmentation and classification of medical images, including segmentation of white matter hyper-intensities ...

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Review of data mining applications for quality assessment in manufacturing industry: Support Vector Machines

Review of data mining applications for quality assessment in manufacturing industry: Support Vector Machines

... weighted support vector machines (WSVM) for automated process monitoring and early fault diagnosis, while original SVM demonstrate poor performance when applied directly to these ...

59

Primary investigation of sound recognition for a domotic application using support vector machines

Primary investigation of sound recognition for a domotic application using support vector machines

... [5]. Support Vector Machines (SVMs) is a hyperplane based method that has gained increasing attention in the pattern recognition community over the last few years and has been successfully applied to ...

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Efficient Seismic fragility curve estimation by Active Learning on Support Vector Machines

Efficient Seismic fragility curve estimation by Active Learning on Support Vector Machines

... The goal of this paper is twofold. First, it is to propose a simple and efficient methodology for estimating non-parametric fragility curves that allows to reduce the cost of mechanical numerical computations by ...

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Bouligand Derivatives and Robustness of Support Vector Machines for Regression

Bouligand Derivatives and Robustness of Support Vector Machines for Regression

... of support vector machines with non-smooth loss ...the support vector machine based on the ε -insensitive loss function, and kernel based quantile regression based on the pinball loss ...

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Cellular automata urban expansion model based on support vector machines

Cellular automata urban expansion model based on support vector machines

... [9] Martens D., Baesens B., Van G., Vanthienen J. (2007). Comprehensible credit scoring models using rule extraction from support vector machines. Eur. J. Oper. Res. 183, 1466– 1476. [10] Liu X., Li ...

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Object Recognition System on Chip Using the Support Vector Machines

Object Recognition System on Chip Using the Support Vector Machines

... constructed vector instructions ...This vector coprocessor architecture is a good compromise between a fully hardwired solution and a fully programmable general-purpose ...

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Analyse automatique de données par Support Vector Machines non supervisés

Analyse automatique de données par Support Vector Machines non supervisés

... This section describes the algorithm F-SMO, inspired from the algorithm proposed by Bordes [4] for SVM classification. The F-SMO algorithm offers two important advantages over SMO. Fir[r] ...

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