Easy multiple kernel learning. ESANN 2014 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp.23-25, 2014. ,
A training algorithm for optimal margin classifiers, Proceedings of the fifth annual workshop on Computational learning theory , COLT '92, pp.144-152, 1992. ,
DOI : 10.1145/130385.130401
URL : http://www.svms.org/training/BOGV92.pdf
Integrated Model of Multiple Kernel Learning and Differential Evolution for EUR/USD Trading, The Scientific World Journal, vol.11, issue.12, p.2014, 2014. ,
DOI : 10.1023/A:1011654624255
URL : http://doi.org/10.1155/2014/914641
Hybrid Method of Multiple Kernel Learning and Genetic Algorithm for Forecasting Short-Term Foreign Exchange Rates, Computational Economics, vol.38, issue.3, pp.1-41, 2013. ,
DOI : 10.1109/ICIC.2009.253
Improving classification performance of Support Vector Machine by genetically optimising kernel shape and hyper-parameters, Applied Intelligence, vol.63, issue.1, pp.280-294, 2012. ,
DOI : 10.1007/s10994-006-6130-8
Structure Discovery in Nonparametric Regression through Compositional Kernel Search, Proceedings of the 30th International Conference on Machine Learning, pp.1166-1174, 2013. ,
Multiple kernel learning algorithms, Journal of Machine Learning Research. ICML, pp.2211-2268, 2011. ,
Optimisation of turning parameters by integrating genetic algorithm with support vector regression and artificial neural networks, The International Journal of Advanced Manufacturing Technology, vol.44, issue.13, pp.1-4, 2014. ,
DOI : 10.1080/00207540600558049
A primal method for multiple kernel learning, Neural Computing and Applications, vol.7, issue.3, pp.3-4, 2012. ,
DOI : 10.1017/CBO9780511809682
The genetic evolution of kernels for support vector machine classifiers, 15th Irish Conference on Artificial Intelligence pp, pp.445-453, 2004. ,
Use of ranks in one-criterion variance analysis, Am. Stat. Assoc. pp, pp.583-621, 1952. ,
DOI : 10.1080/01621459.1952.10483441
Genetic Algorithms for Support Vector Machine Model Selection. The, IEEE International Joint Conference on Neural Network Proceedings pp, pp.3063-3069, 2006. ,
Application of GA???SVM method with parameter optimization for landslide development prediction, Natural Hazards and Earth System Science, vol.14, issue.3, pp.525-533, 2014. ,
DOI : 10.5194/nhess-14-525-2014
URL : https://doi.org/10.5194/nhessd-1-5295-2013
A GA-SVM hybrid classifier for multiclass fault identification of drivetrain gearboxes. 2014 IEEE Energy Conversion Congress and Exposition, ECCE, pp.3894-3900, 2014. ,
DOI : 10.1109/ecce.2014.6953930
URL : http://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1266&context=electricalengineeringfacpub
More Generality in Efficient Multiple Kernel Learning Manik pp, pp.1065-1072, 2009. ,
DOI : 10.1145/1553374.1553510
URL : http://www.research.microsoft.com/~manik/pubs/varma09.pdf
Functions of positive and negative type, and their connection the theory of integral equations, Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, vol.209, pp.441-458, 1909. ,
DOI : 10.1098/rspa.1909.0075
URL : http://rspa.royalsocietypublishing.org/content/royprsa/83/559/69.full.pdf
Data analysis, including statistics, In Handbook of Social Psychology, p.2011, 1968. ,
, SimpleMKL. Journal of Machine Learning Research, vol.9, pp.2491-2521, 2008.
The CRISP-DM Model: The New Blueprint for Data Mining, Journal of Data Warehousing, pp.13-22, 2000. ,
Recognition of communication signal types using genetic algorithm and support vector machines based on the higher order statistics, Digital Signal Processing, vol.20, issue.6, pp.1748-1757, 2010. ,
DOI : 10.1016/j.dsp.2010.03.003
Combining multiple kernels for efficient image classification. Applications of Computer Vision WACV, pp.1-8, 2009. ,
Support-Vector Networks, 1995. ,
Simple and efficient multiple kernel learning by group lasso, International Conference on Machine Learning (ICML) pp, pp.1191-1198, 2010. ,