[PDF] Top 20 Gene selection using support vector machines with nonconvex penalty
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Gene selection using support vector machines with nonconvex penalty
... The objective function in (3) consists of the hinge loss part and the SCAD penalty on the directional vector w . The para- meter λ balances the trade-off between data fitting and model parsimony. If λ is ... See full document
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Support vector machines applied to the genetic classification problem of hybrid populations with high degrees of similarity
... Genetic diversity analyses provide an opportunity for plant breeders to develop new and improved cultivars with desirable characteristics (Govindaraj et al., 2015). The selection of appropriate genitors in ... See full document
10
Robustness and Regularization of Support Vector Machines
... regularization penalty) is an upper bound of the minimax error with respect to certain set-inclusive ...suggests using the robustness view to derive sharp sample complexity bounds for a broad class of ... See full document
26
Filtered selection coupled with support vector machines generate a functionally relevant prediction model for colorectal cancer
... On the other hand, the hybrid mRMR + REPT was the best performing model using the tenfold cross validation. This is because REPT is a fast decision tree learning by downsizing of decision trees. It removes ... See full document
13
Working Set Selection Using Second Order Information for Training Support Vector Machines
... set selection could reduce the number of iterations and hence are an important research ...that using second order information generally leads to faster ...these selection methods are only heuristics ... See full document
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Support Vector Machines Networks to Hybrid Neuro Genetic SVMs in Portfolio Selection
... to support corporate financial analysis [1]-[9]. Support Vector Machines in a hybrid with Genetic Algorithms optimization provide efficient results of financial analysis in ... See full document
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A comparison of random forests, boosting and support vector machines for genomic selection
... We used regression trees as basis functions. Boosting regression trees involves generating a sequence of trees, each grown on the residuals of the previous tree [5,9]. Prediction is accomplished by weighting the ensemble ... See full document
5
Support vector machines with adaptive Lq penalty
... standard Support Vector Machine (SVM) minimizes the hinge loss function subject to the L 2 penalty or the roughness ...variable selection by producing sparse solutions (Bradley and Man- ... See full document
24
Covering Numbers and Support Vector Machines
... SV machines which use Gaussian radial basis function (RBF) kernels with variance ...order selection possible using any parameterized family of kernel functions, since it describes how the capacity of ... See full document
12
An Information Criterion for Variable Selection in Support Vector Machines
... We compare the performance of the new methods with that of the other discussed criteria on several real-world data sets. We use some of the benchmark data sets used in Rakotomamonjy (2003), and in R¨atsch et al. (2001). ... See full document
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Classification of power disturbances using multilevel support vector Machine
... This paper proposed a prototype of wavelet-based support vector machine classifiers for power disturbance recognition and classification. The proposed method can reduce the quantity of extracted features of ... See full document
5
Preventing Over-Fitting during Model Selection via Bayesian Regularisation of the Hyper-Parameters
... model selection, due to the variance of the model selection ...the selection criterion using the elliptical RBF kernel is lower than that achievable us- ing the spherical RBF kernel, however ... See full document
21
Molecular discrimination of responders and nonresponders to anti TNFalpha therapy in rheumatoid arthritis by etanercept
... this gene set was associated with distinct clinical responses as evinced by changes in overall disease activities 3 months after the start of ...significant using a resampling ... See full document
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Boosting methods for variable selection in high dimensional sparse models
... variable selection, we report two types of selection errors. Selection error I is defined as the number of non-zero coefficients which are estimated as zero, and selection error II is the ... See full document
77
STAGE-DISCHARGE MODELING USING SUPPORT VECTOR MACHINES
... discharge using a hypothetical data set in order to model a looped-rating ...set using both kernels are given in Table 2. The results obtained using both kernels are given in Table ...by using ... See full document
9
Support Vector Machines for Face Recognition
... at using a database of 2,500 pictures of 16 people under all blends of 3 head presentations, 3 head sizes or scales, and 3 lighting conditions and diverse ... See full document
13
Using Least Squares Support Vector Machines for Frequency Estimation
... Frequency estimation is transformed to a pattern recognition problem, and a least squares support vector machine (LS-SVM) estimator is derived. The estimator can work efficiently without the need of ... See full document
5
Speaker verification using sequence discriminant support vector machines
... Each component of the score-space corresponds to the derivative of the log-likelihood score with respect to one of the parameters of the model. In some ways, it is a measure of how well the sequence matches the model. ... See full document
9
High Performance Implementation of Support Vector Machines Using OpenCL
... In [27], OpenCL is used to apply a multi-GPU system to accelerate backprojection for image reconstruction. OpenCL is chosen for this work because of its independence from hardware, vendor and platform related ... See full document
101
Support vector machines for texture classification
... Many methods have been developed to extract textural features, which can loosely be classified as statistical, model-based, and signal processing methods. In statistical approaches, textures are described using ... See full document
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