A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
Filters
HyParSVM – A New Hybrid Parallel Software for Support Vector Machine Learning on SMP Clusters
[chapter]
2006
Lecture Notes in Computer Science
Preserved Fulltext
With this extention we obtained a flexible parallel SVM algorithm that can be used on high-end machines with SMP architectures to process the large data sets that arise more and more in bioinformatics ...
In this paper we describe a new hybrid distributed/shared memory parallel software for support vector machine learning on large data sets. ...
-A distributed SVM algorithm for row-wise and column-wise data distribution is described in [26] , which so far can be used for linear SVMs only. ...
doi:10.1007/11823285_36
fatcat:d6lmqilj3nhntkjwuxii2lt7x4
Biomedical Classification Problems Automatically Solved by Computational Intelligence Methods
2020
IEEE Access
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
INDEX TERMS Biomedical classification problems, estimation of distribution algorithm, evolutionary algorithms, genetic programming, orthogonal polynomial kernels, support vector machines. ...
To deal with this complexity, a systematic methodology for selecting a suitable model for a given classification problem is required. ...
Arturo González-Vega for his valuable comments about experimental results. ...
doi:10.1109/access.2020.2998749
fatcat:qufxajj66nampin3anpzfeqbhq
Extensions of the SVM Method to the Non-Linearly Separable Data
2013
Informatică economică
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
Several approaches based on genetic search in solving the more general problem of identifying the optimal type of kernel from pre-specified set of kernel types (linear, polynomial, RBF, Gaussian, Fourier ...
The currently used methods for solving the resulted QP-problem require access to all labeled samples at once and a computation of an optimal solution is of complexity O(N 2 ). ...
A new class of approaches contains algorithms, referred as Genetic Algorithms-SVM (GA-SVM or GSVM), and Hybrid Genetic Algorithms SVM (HGA-SVM). ...
doi:10.12948/issn14531305/17.2.2013.14
fatcat:khh6pos3arecrl2ka257udlr4a
Quadratically constrained quadratic programming for classification using particle swarms and applications
[article]
2014
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Particle swarm optimization is used in several combinatorial optimization problems. In this work, particle swarms are used to solve quadratic programming problems with quadratic constraints. ...
The optimization problem is solved in distributed format using modified particle swarms. ...
Such solvers are used for solving SVM in distributed format [13] . Table 1 presents a pseudo code for the algorithm. ...
arXiv:1407.6315v1
fatcat:o3znuubmufacned54g6sfqxrfq
A New Method on Software Reliability Prediction
2013
Mathematical Problems in Engineering
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
As we all know, relevant data during software life cycle can be used to analyze and predict software reliability. ...
And then based on analyzing classic PSO-SVM model and the characteristics of software reliability prediction, some measures of the improved PSO-SVM model are proposed, and the improved model is established ...
to be feasible in solving the SVM quadratic programming problem, but the research is fit for the large sample set, which is ineffective for less sample data in early software reliability prediction. ...
doi:10.1155/2013/385372
fatcat:lkvwl6a4zjdu7j4urmabq2wfaq
R/DWD: distance-weighted discrimination for classification, visualization and batch adjustment
2012
Bioinformatics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
In addition, R/DWD also provides efficient solvers for second-order-cone-programming and quadratic programming. Availability and implementation: The package is freely available from cran.r- ...
DWD has proven to be very useful for several fundamental bioinformatics tasks, including classification, data visualization and removal of biases, such as batch effects. ...
The optimization problem that underlies SVM is called quadratic programming (QP). ...
doi:10.1093/bioinformatics/bts096
pmid:22368246
pmcid:PMC3324517
fatcat:37cvp3qll5fvli6553g5ykfwcu
Advances in the Application of Machine Learning Techniques in Drug Discovery, Design and Development
[chapter]
2006
Advances in Intelligent and Soft Computing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2006; you can also visit the original URL.
The file type is application/pdf.
Machine learning tools, in particular support vector machines (SVM), Particle Swarm Optimisation (PSO) and Genetic Programming (GP), are increasingly used in pharmaceuticals research and development. ...
They are inherently suitable for use with 'noisy', high dimensional (many variables) data, as is commonly used in cheminformatic (i.e. ...
The authors wish to thank GSK colleagues, past and present, for their efforts in expressing the nature of their research. ...
doi:10.1007/978-3-540-36266-1_10
fatcat:gdq5kxmbfjfbbaccdcjpsopg4e
Efficient optimization of support vector machine learning parameters for unbalanced datasets
2006
Journal of Computational and Applied Mathematics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
Traditionally, grid search techniques have been used for determining suitable values for these parameters. ...
Support vector machines are powerful kernel methods for classification and regression tasks. If trained optimally, they produce excellent separating hyperplanes. ...
We also thank Gaetano Zanghirati and Luca Zanni for advice in the implementation of the projection method including the inner solver, and the unknown referee for his valuable comments. ...
doi:10.1016/j.cam.2005.09.009
fatcat:7on6ktkqx5cetnw5phacawt6pu
A Survey on SVM Classifiers for Intrusion Detection
2014
International Journal of Computer Applications
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2016; you can also visit the original URL.
The file type is application/pdf.
Here, we are going to propose Intrusion Detection System using data mining technique: Support Vector Machine (SVM). ...
In this paper how the support vector machines are used for intrusion detection are described and finally proposed a solution to the inrusion detection system. ...
Feature selection or attribution reduction can help reduce the SVM classification time and saving memory space effectively.In future genetic algorithm and rough set theory combinely apply to SVM for enhancing ...
doi:10.5120/17294-7779
fatcat:kyj36tilq5bntalx2adtquv3xa
Rapid Prediction of Bacterial Heterotrophic Fluxomics Using Machine Learning and Constraint Programming
2016
PLoS Computational Biology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
We performed a grid search of the best parameter set for each algorithm and verified their performance through 10-fold cross validations. SVM yields the highest accuracy among all three algorithms. ...
Further, we employed quadratic programming to adjust flux profiles to satisfy stoichiometric constraints. ...
The prediction on 29 fluxes is done via an RBF-kernel SVM, whose outcome will be finalized by quadratic programming. ...
doi:10.1371/journal.pcbi.1004838
pmid:27092947
pmcid:PMC4836714
fatcat:szhpazqiknch3pl3d7iwq7cuim
A GA-SVM feature selection model based on high performance computing techniques
2009
2009 IEEE International Conference on Systems, Man and Cybernetics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
In this paper, an HPC-enabled GA-SVM (HGA-SVM) is proposed by integrating data parallelization, multithreading and heuristic techniques with the ultimate goal of robustness and low computational cost. ...
However, the high computational cost strongly discourages the application of GA-SVM in large-scale datasets. ...
Parallel SVM SVM training is compute-intensive because it requires quadratic programming (QP) [1] for determining the optimal separating hyperplane. ...
doi:10.1109/icsmc.2009.5346120
dblp:conf/smc/ZhangFGKL09
fatcat:7yc5u6dqcfhctnr4zhxblr3x3y
Review on: Twin Support Vector Machines
2014
Annals of Data Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
Twin support vector machine (TWSVM), an useful extension of the traditional SVM, becomes the current researching hot spot in machine learning during the last few years. ...
For the binary classification problem, the basic idea of TWSVM is to seek two nonparallel proximal hyperplanes such that each hyperplane is closer to one of the two classes and is at least one distance ...
Natural Science Foundation of China (Nos. 11271361, 61472390, 61402429, 71331005), Major International (Regional) Joint Research Project (No. 71110107026), the Ministry of water resources' special funds for ...
doi:10.1007/s40745-014-0018-4
fatcat:o5lkkilovfct3cwwdlxhmvxft4
Parallel multiclass stochastic gradient descent algorithms for classifying million images with very-high-dimensional signatures into thousands classes
2014
Vietnam Journal of Computer Science
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
We propose (1) a balanced training algorithm for learning binary SVM-SGD classifiers, and (2) a parallel training process of classifiers with several multi-core computers/grid. ...
We extend the stochastic gradient descent (SGD) for support vector machines (SVM-SGD) in several ways to develop the new multiclass SVM-SGD for efficiently classifying large image datasets into many classes ...
The plane (w, b) is obtained by solving the quadratic programming (1). ...
doi:10.1007/s40595-013-0013-2
fatcat:5cqdbgvkgjbmvd7ndvnifkiyxi
A nested heuristic for parameter tuning in Support Vector Machines
2014
Computers & Operations Research
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
Second, as algorithmic requirements we only need either an SVM library or any routine for the minimization of convex quadratic functions under linear constraints. ...
The default approach for tuning the parameters of a Support Vector Machine (SVM) is a grid search in the parameter space. ...
Alpaydın for kindly providing the results for the benchmarking methods used in Sections 3.3.2 and 3.3.3, which were not available in [22] . ...
doi:10.1016/j.cor.2013.10.002
fatcat:upsru2oopvehznczyicanxqcs4
A single pairwise model for classification using online learning with kernels
2017
Hacettepe Journal of Mathematics and Statistics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
This modied algorithm is suitable for large data sets due to its online nature and it can also handle the sparsity structure existing in the data. ...
Furthermore, a general framework is designed to use this pairwise approach in a multi-class classication task. ...
It is also related to the sequential minimal optimization (SMO) [18] algorithm and converges to the solution of the SVM quadratic programming problem. ...
doi:10.15672/hjms.2017.416
fatcat:2vlvp47w3raxtckedsq6qhn32m
« Previous
Showing results 1 — 15 out of 401 results