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HyParSVM – A New Hybrid Parallel Software for Support Vector Machine Learning on SMP Clusters [chapter]

Tatjana Eitrich, Wolfgang Frings, Bruno Lang
2006 Lecture Notes in Computer Science  
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

Luis Carlos Padierna, Carlos Villasenor-Mora, Silvia Alejandra Lopez Juarez
2020 IEEE Access  
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

Luminita STATE, Catalina COCIANU, Cristian USCATU, Marinela MIRCEA
2013 Informatică economică  
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]

Deepak Kumar, A G Ramakrishnan
2014 arXiv   pre-print
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

Zhang Xiaonan, Yang Junfeng, Du Siliang, Huang Shudong
2013 Mathematical Problems in Engineering  
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

Hanwen Huang, Xiaosun Lu, Yufeng Liu, Perry Haaland, J.S. Marron
2012 Bioinformatics  
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]

S. J. Barrett, W. B. Langdon
2006 Advances in Intelligent and Soft Computing  
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

Tatjana Eitrich, Bruno Lang
2006 Journal of Computational and Applied Mathematics  
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

R. RavinderReddy, B. Kavya, Y Ramadevi
2014 International Journal of Computer Applications  
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

Stephen Gang Wu, Yuxuan Wang, Wu Jiang, Tolutola Oyetunde, Ruilian Yao, Xuehong Zhang, Kazuyuki Shimizu, Yinjie J. Tang, Forrest Sheng Bao, Christos A. Ouzounis
2016 PLoS Computational Biology  
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

Tianyou Zhang, Xiuju Fu, R.S.M. Goh, Chee Keong Kwoh, G.K.K. Lee
2009 2009 IEEE International Conference on Systems, Man and Cybernetics  
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

Yingjie Tian, Zhiquan Qi
2014 Annals of Data Science  
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

Thanh-Nghi Do
2014 Vietnam Journal of Computer Science  
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

Emilio Carrizosa, Belén Martín-Barragán, Dolores Romero Morales
2014 Computers & Operations Research  
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

Engin Tas
2017 Hacettepe Journal of Mathematics and Statistics  
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
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