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Tuning and evolution of support vector kernels

Patrick Koch, Bernd Bischl, Oliver Flasch, Thomas Bartz-Beielstein, Claus Weihs, Wolfgang Konen
2012 Evolutionary Intelligence  
Kernel-based methods like Support Vector Machines (SVM) have been established as powerful techniques in machine learning.  ...  Design and hand-tuning of kernel functions can be time-consuming and requires expert knowledge.  ...  [40, 14, 47] Conclusion In this paper we analyzed the tuning and evolution of Support Vector Machines.  ... 
doi:10.1007/s12065-012-0073-8 fatcat:mkvbwojnffbhtnaosgfht752fy

Evolutionary tuning of multiple SVM parameters

Frauke Friedrichs, Christian Igel
2005 Neurocomputing  
In particular, more flexibility of the kernel can reduce the number of support vectors.  ...  The problem of model selection for support vector machines (SVMs) is considered.  ...  We give a short description of SVMs in Section 2 and of the CMA-ES in Section 3. The parameterization of general Gaussian kernels is introduced in Section 4.  ... 
doi:10.1016/j.neucom.2004.11.022 fatcat:5zuwmxal4nblvbphi4hyeuz23a

Support vector machine learning with an evolutionary engine

R Stoean, M Preuss, C Stoean, E El-Darzi, D Dumitrescu
2009 Journal of the Operational Research Society  
Additionally, the simultaneous evolution of the hyperplane and of nonstandard kernels can be achieved.  ...  Making indefinite kernel learning practical. Technical Report, University of Dortmund. Smola AJ and Scholkopf B (1998). A tutorial on support vector regression.  ... 
doi:10.1057/jors.2008.124 fatcat:hverv7musncgvmz2iuqeyn7rhm

Biomedical Classification Problems Automatically Solved by Computational Intelligence Methods

Luis Carlos Padierna, Carlos Villasenor-Mora, Silvia Alejandra Lopez Juarez
2020 IEEE Access  
Our proposal, including state-of-the-art evolutionary algorithms and support vector machines, is tested on a variety of biomedical problems.  ...  INDEX TERMS Biomedical classification problems, estimation of distribution algorithm, evolutionary algorithms, genetic programming, orthogonal polynomial kernels, support vector machines.  ...  ACKNOWLEDGMENT The authors would like to acknowledge the support provided by the División de Ciencias e Ingenierías, Universidad de Guanajuato, Campus León during the research and preparation of the manuscript  ... 
doi:10.1109/access.2020.2998749 fatcat:qufxajj66nampin3anpzfeqbhq

Support vector regression with parameter tuning assisted by differential evolution technique: Study on pressure drop of slurry flow in pipeline

Sandip Kumar Lahiri, Kartik Chandra Ghanta
2009 Korean Journal of Chemical Engineering  
The method incorporates hybrid support vector regression and differential evolution technique (SVR-DE) for efficient tuning of SVR meta parameters.  ...  This paper describes a robust support vector regression (SVR) methodology that offers superior performance for important process engineering problems.  ...  In the present work, we propose a hybrid support vector regression-differential evolution (SVR-DE) approach for tuning the SVR meta parameters and illustrate it by applying it for predicting the pressure  ... 
doi:10.1007/s11814-009-0195-6 fatcat:mj6ag6t26fbahmg4hnht23sqf4

The Support Vector Regression with the parameter tuning assisted by a differential evolution technique: Study of the critical velocity of a slurry flow in a pipeline

S.K. Lahiri, K.C. Ghanta
2008 Chemical Industry and Chemical Engineering Quarterly  
The method incorporates hybrid support vector regression and a differential evolution technique (SVR-DE) for the efficient tuning of SVR meta parameters.  ...  This paper describes a robust Support Vector regression (SVR) methodology, which can offer a superior performance for important process engineering problems.  ...  In the present work, we propose a hybrid support vector regression-differential evolution (SVR-DE) approach for tuning the SVR meta parameters and illustrate it by applying it for predicting the critical  ... 
doi:10.2298/ciceq0803191l fatcat:l37pcqezqjbz7oswt67ppdgfqy

Page 1122 of The Journal of the Operational Research Society Vol. 60, Issue 8 [page]

2009 The Journal of the Operational Research Society  
Additionally, the simultaneous evolution of the hyperplane and of nonstandard kernels can be achieved.  ...  Making indefinite kernel learning practical. Technical Report, University of Dortmund. Smola AJ and Scholkopf B (1998). A tutorial on support vector regression.  ... 

Toward a Solution for "Genetic Algorithm"-"Support Vector Machine" Combination to Have a Reliable P300-based BCI

Gholamreza Salimi Khorshidi, Ayyoub Jaafari, Ali Motie Nasrabadi, Mohammadreza Hashemi Golpayeghani, Caro Lucas
2007 European Society for Fuzzy Logic and Technology  
In this study, we present a new framework to combine the Genetic Algorithm (GA) and Support Vector Machine (SVM) for an accurate P300 detection.  ...  SVM classifiers have two important parameters to be tuned; C and σ (standard deviation of the Gaussian kernel).  ... 
dblp:conf/eusflat/KhorshidiJNGL07 fatcat:pciirjodyvaqvcdrypjirhvjwm

Unsupervised Speaker Indexing Using One-Class Support Vector Machines

M. Davy, Belkacem Fergani, Amrane Houacine
2006 Zenodo  
Publication in the conference proceedings of EUSIPCO, Florence, Italy, 2006  ...  Figure 3 : 3 Figure 3: Evolution of the DER for the KCD algorithm when the kernel width parameter σ kernel increases.  ...  In practice, tuning ν is even more intuitive that tuning λ. For example, ν = 0.2 means that at most (and asymptotically exactly) 20% of the feature vectors in X are outliers.  ... 
doi:10.5281/zenodo.53403 fatcat:e5xqaumubnejljmv7opefqroie

An Artificial Intelligence Approach for Groutability Estimation Based on Autotuning Support Vector Machine

Hong-Hai Tran, Nhat-Duc Hoang
2014 Journal of Construction Engineering  
Meanwhile, the differential evolution (DE) optimization algorithm is employed to identify the optimal tuning parameters of the SVM algorithm, namely, the penalty parameter and the kernel function parameter  ...  In the new model, the support vector machine (SVM) algorithm is utilized to classify grouting activities into two classes: success and failure.  ...  Conflict of Interests The authors declare that there is no conflict of interests regarding the publication of this paper.  ... 
doi:10.1155/2014/109184 fatcat:fgozoqy6wbctji7dw25j3dnlom

Building sparse representations and structure determination on LS-SVM substrates

K. Pelckmans, J.A.K. Suykens, B. De Moor
2005 Neurocomputing  
This paper studies a method to obtain sparseness and structure detection for a class of kernel machines related to Least Squares Support Vector Machines (LS-SVMs).  ...  This regularization trade-off is tuned at higher levels such that sparse representations and/or structure detection are obtained.  ...  This research work was carried out at the ESAT laboratory of the Katholieke Universiteit Leuven. It is supported by grants from several funding agencies and sources:  ... 
doi:10.1016/j.neucom.2004.11.029 fatcat:qrbmcq3isnhxjh4w6nwk3miwoy

The Public Sentiment and Emotional Variations in Social Media using Twitter Dataset

2019 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
The Learning models to be built using the Support Vector Tool (SVT) classification method with a kernel trick applied with composition using unigram, bigram and hybrid (unigram + bigram) features.  ...  Limited use of transforms (kernels) for linearity in higher dimensional spaces and lack of parameter tuning. In most of the real time dataset the neutral class is very high.  ...  In particular, it is commonly utilized in support vector tool relegation. Fig. 10 . 10 Fig. 10. Radial-Basis Program kernel Tuning the parameters(C and gamma) of SVT kernels.  ... 
doi:10.35940/ijitee.l3358.1081219 fatcat:p7cu7cbxozbkpj565l6z3m33gm

A TWO-STAGE EVOLUTIONARY APPROACH FOR EFFECTIVE CLASSIFICATION OF HYPERSENSITIVE DNA SEQUENCES

UDAY KAMATH, AMARDA SHEHU, KENNETH A. DE JONG
2011 Journal of Bioinformatics and Computational Biology  
Kernel evolution algorithm Population Size = 2000, Generations = 30, Initialization = Half and Half method, Crossover = Subtree with probability 0.8, Mutation = Grow with probability of 0.2, Elites = 10  ...  Statistical learning techniques like Support Vector Machines (SVM), for instance, are employed to classify DNA sequences as HS or non-HS.  ...  Acknowledgments We are indebted to Sean Luke and Keith Sullivan for insights on GP and Rezarta Dogan and Sarang Kayande for discussions on this work.  ... 
doi:10.1142/s0219720011005586 pmid:21714132 fatcat:4ewal2szijbfnficz3yicckgxm

Genetic Algorithms for Support Vector Machine Model Selection

S. Lessmann, R. Stahlbock, S.F. Crone
2006 The 2006 IEEE International Joint Conference on Neural Network Proceedings  
Striving to automate model selection for support vector machines we apply a metastrategy utilizing genetic algorithms to learn combined kernels in a data-driven manner and to determine all free kernel  ...  Support vector machines belong to the group of semiparametric classifiers.  ...  This involved the construction of a combined kernel and the tuning of all resulting parameters.  ... 
doi:10.1109/ijcnn.2006.247266 dblp:conf/ijcnn/LessmannSC06 fatcat:asue3oppqndinccwccgmzxub7y

Evolution strategies-tuned support vector machine-based classification of inter-area oscillations

Adamantios Marinakis, Carsten Franke, Mats Larsson
2014 2014 Power Systems Computation Conference  
Evolution strategies are used to tune the SVM hyperparameters, including the selection of its kernel function, such that the accuracy of the resulting model is as high as possible.  ...  As a step towards this direction, this paper describes the construction of a support vector machine model trained to classify potential operating points according to their corresponding oscillation damping  ...  ACKNOWLEDGEMENT The financial support from the Marie Curie FP7-IAPP project "Using real-time measurements for monitoring and management of power transmission dynamics for the Smart Grid -REAL-SMART", Contract  ... 
doi:10.1109/pscc.2014.7038317 dblp:conf/pscc/MarinakisFL14 fatcat:tty62juyifbs7ltrt5nhbnow3m
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