[PDF] Top 20 Laplacian Support Vector Machines Trained in the Primal
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Laplacian Support Vector Machines Trained in the Primal
... the primal training approach. 4 The last column refers to LapSVMs trained using the best (in terms of accuracy) of the proposed stopping heuristics for each specific data ...the primal by the ... See full document
36
Quadratic Surface Support Vector Machines with Applications.
... be trained using a set of training points and their associated weights, where each weight indicates the relative importance of the corresponding training ... See full document
113
Support vector machines for texture classification
... All textures were originally gray-scale images with 256 levels. The texture classifier was trained on randomly selected portions of 256256 subimages of texture images that were not included in the test images. To ... See full document
9
“What Is Your Evidence?” A Study of Controversial Topics on Social Media
... to support their ...that Support Vector Machines (SVM) classifiers trained with n-grams and other features capture the different types of evidence used in social media and demonstrate ... See full document
11
Screening PubMed abstracts: is class imbalance always a challenge to machine learning?
... We trained four binary text classifiers (support vector machines, k-nearest neighbor, random forest, and elastic-net regularized generalized linear models) in combination with four techniques ... See full document
9
Areas categorization by operating Support Vector Machines
... is trained to be able to isolate each class from all ...fewer support vectors than a multi-class cataloging based on several binary ...n(n-1)/2 machines to be ... See full document
9
Consensus-Based Distributed Support Vector Machines
... Remark 3 Compared to existing centralized online SVM alternatives in, for example, Cauwen- berghs and Poggio (2000) and Fung and Mangasarian (2002), the online MoM-DSVM algorithm of this section allows seamless ... See full document
45
Infinite ensemble learning with support vector machines
... First, we can group these kernels by the distance metrics they use. The stump kernel and the Laplacian kernel uses the one-norm distance between vectors, while the others uses the two-norm distance. An advantage ... See full document
83
Sparse Deconvolution Using Support Vector Machines
... called primal signal model (PSM) algorithm, does not ensure sparseness in the estimated unknown signal, but it highlights the implicit relationship between the impulse response and its autocorrelation with ... See full document
13
Support Vector Machines for Face Recognition
... 55], Laplacian Eigenmap [56], Locality Preserving Projection (LPP) [57], Embedded Manifold [58], Nearest Manifold Approach [59],Discriminant Manifold Learning [60] and Laplacianfaces ... See full document
13
A Hierarchy of Support Vector Machines for Pattern Detection
... In the context of finding faces in cluttered scenes, Fleuret and Geman (2001) developed a fast, coarse-to-fine detector based on simple edge configurations and a hierarchical decomposition of the space of poses ... See full document
37
Online Full Text
... the support vector machines are an approximate implementation of the method of structural risk ...a support vector machine does not provide domain-specific knowledge, it provides good ... See full document
7
Continuous decoding of grasping tasks for a prospective implantable cortical neuroprosthesis
... A Support Vector Machines (SVMs) classifier has been trained for a continuous decoding of four/six grip types using signals recorded in two monkeys from motor neurons of the ventral prem[r] ... See full document
6
Genomic prediction of celiac disease targeting HLA-positive individuals
... L1/L2-regularized support-vector machines trained on large European case-control datasets, we constructed novel CD GRSs specific to individuals with ... See full document
11
Dimensionality Reduction via Sparse Support Vector Machines (Kernel Machines Section)
... VS-SSVM proved to be very effective on problems in drug design. The number of variables was dramatically reduced while maintaining or even improving the generalization ability. This method outperforms SVMs trained ... See full document
15
Chunking with Support Vector Machines
... are trained using distinct chunk rep- resentations and different machine learning algo- rithms, such as MBL, ME and IGTree(Tjong Kim Sang, 2000a; Tjong Kim Sang et ... See full document
8
A Novel Approach to Design the Intelligent Technique for Intrusion Detection In Cloud
... Abstract— In the cloud computing, security mechanisms are not mature enough to protect the data stored in the cloud. Hence, it is necessary to propose efficient methods for providing security to the data stored in the ... See full document
5
Clustering Via Supervised Support Vector Machines
... The choices of the SVM regularization constant, C, has a profound distinction when used in the context of SVM-Relabeler. The choice of C in the supervised SVM controls the trade off between the training error and the ... See full document
93
Support vector machines in projects risk classification
... Matrix (PIMs), also called risk matrices (Cox, 2008). The studies of Cox (2008); Markowski and Mannan (2008), Ni, Chen and Chen (2010) and Duijim (2015) showed that PIMs have deficiencies that can lead to a wrong ... See full document
6
Support Vector Machines for Design Space Exploration
... For linear problems factor analysis or principal component analysis (PCA) are well established methods for handling high dimensional data and performing dimensionality reduc- tion, e.g. as preprocessing tools for solving ... See full document
6
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