[PDF] Top 20 Twin Support Vector Machines Based on the Mixed Kernel Function
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Twin Support Vector Machines Based on the Mixed Kernel Function
... In 2007, Twin Support Vector Machines (TWSVM) was proposed by Jayadeva[16] et al. The solution of binary classification problem is converted to the solution of two smaller quadratic ... See full document
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Mixed Kernel Twin Support Vector Machines Based on the Shuffled Frog Leaping Algorithm
... the mixed kernel function, MK-TWSVM can be able to achieve such significant ...This mixed kernel function does not randomly select any kernel functions to be combined, but ... See full document
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Polynomial Smooth Twin Support Vector Machines Based on Invasive Weed Optimization Algorithm
... algorithm. Based on the above idea, a novel version for smooth TWSVM, termed polynomial smooth twin support vector machines (PSTWSVM), is proposed in this ... See full document
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Comparison Of Various Kernels Of Support Vector Machine
... field. Support vector machine is the popular algorithm for classification and ...by support vector machine, LIBSVM is being used as a tool. Support vector machine classifies the ... See full document
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Sparseness of Support Vector Machines
... polynomial kernel it is easy to see that the representation (3) is not unique whenever the sample size n is too ...its support vectors does not exceed the (finite!) dimension of the RKHS of the polynomial ... See full document
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A Modified Cosine Similarity based Log Kernel for Support Vector Machines in the Domain of Text Classification
... SVM kernel has been done in the past (Lodhi et ...2013). Kernel (a similarity function which takes two input feature vectors and find out how similar they are) boost the performance of SVM especially ... See full document
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FUZZY BASED DETECTION AND SWARM BASED AUTHENTICATED ROUTING IN MANET
... the Support vector machines for ...of kernel functions and the classification error and accuracy are noted in each ...basis function and ...RBF kernel gives the best performance ... See full document
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Chunking with Support Vector Machines
... In our experiments, in order to obtain higher ac- curacy, we also apply weighted voting of 8 SVM- based systems which are trained using distinct chunk representations. Before applying weighted voting method, first ... See full document
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A Fast And Energy Efficient Path Planning Algorithm For Offline Navigation Using Svm Classifier
... Tree based Classification ...using kernel functions and a new method of NSVM decision tree is proposed based on traditional SVM decision ...scheme based on ANN and SVM for protection of power ... See full document
5
MCs Detection with Combined Image Features and Twin Support Vector Machines
... objective function of (7) or (8) is the sum of squared distances from the hyperplane to points of one ...objective function minimizes the sum of error variables, thus attempting to minimize ... See full document
7
On a Class of Support Vector Kernels based on Frames in Function Hilbert Spaces
... different Support Vector algorithms (Vapnik 1998; Smola and Sch¨ olkopf 1998; Smola et ...other kernel based methods (Sch¨ olkopf et ...A Support vector machine (SVM) is a ... See full document
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Fast Kernel Classifiers with Online and Active Learning
... loss function because it discards distant outliers, (iii) active selection leads to more sparse kernel expansions with better generalization abilities (Cesa-Bianchi et ... See full document
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Online Full Text
... for support vector machines in a hybrid Data Mining and Case-Based Reasoning system which incorporates a vector model to help transfer textual information to numerical vector in ... See full document
5
An Analog Circuit Diagnosis Method Based on Adaptive-Kernel ICA
... (MDFS). Based on the comprehensive consideration of MDFS method according to the specific circuit, we suppose the circuit occurs to soft fault that the values of the circuit components deviate 10% from the normal ... See full document
5
Clustering Via Supervised Support Vector Machines
... Although the internal approach to SVM (see svm-internal) clustering is only weakly biased towards the shape of the clusters in the input space (the bias is for spherical clusters in the feature space), it still lacks ... See full document
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Consensus-Based Distributed Support Vector Machines
... The novel approach pursued in the present paper trains an SVM in a fully distributed fashion that does not require a central processing unit. The centralized SVM problem is cast as a set of coupled decentralized convex ... See full document
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Prediction of Drosophila melanogaster gene function using Support Vector Machines
... the function of un-annotated genes. The analysis of such data is based on the premise that genes involved in a particular biological, molecular, and/or biochemical process are often ...grouped based ... See full document
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Analog Circuit Feasibility Modeling using Support Vector Machine with Efficient Kernel Functions
... Proposal in [11] is for active learning scheme for feasibility design space identification. The proposed methodology uses a committee of classifiers to exclude a large portion of entire design space and samples only the ... See full document
6
Finding kernel function for stock market prediction with support vector regression
... a support vector method for density support estimation was introduced by (Scholkopf et al, 2001), and has been successfully applied to a number of problems, including stock market predictions and ... See full document
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Landslide susceptibility modeling based on GIS and novel bagging-based Kernel logistic regression
... bagging-based kernel logistic regression classification m odel w as proposed and applied for landslide susceptibility in Shangnan County, C ...es based on L S S V M ... See full document
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