OLLAWV: OnLine Learning Algorithm using Worst-Violators
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Vojislav Kecman | Sebastián Ventura | Gabriella Melki | Alberto Cano | Sebastián Ventura | V. Kecman | Alberto Cano | Gabriella Melki
[1] Vojislav Kecman,et al. Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning , 2006, Studies in Computational Intelligence.
[2] Gert Cauwenberghs,et al. Incremental and Decremental Support Vector Machine Learning , 2000, NIPS.
[3] Vojislav Kecman. Fast online algorithm for nonlinear support vector machines and other alike models , 2016, Optical Memory and Neural Networks.
[4] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[5] Shai Ben-David,et al. Understanding Machine Learning: From Theory to Algorithms , 2014 .
[6] Bernhard Schölkopf,et al. Learning with kernels , 2001 .
[7] Vojislav Kecman,et al. Algorithms for direct L2 support vector machines , 2014, 2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings.
[8] Ling Jian,et al. A chunk updating LS-SVMs based on block Gaussian elimination method , 2017, Appl. Soft Comput..
[9] Stephen P. Boyd,et al. Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.
[10] Alexander J. Smola,et al. Online learning with kernels , 2001, IEEE Transactions on Signal Processing.
[11] Tong Zhang,et al. Solving large scale linear prediction problems using stochastic gradient descent algorithms , 2004, ICML.
[12] Vojislav Kecman,et al. Multi-target support vector regression via correlation regressor chains , 2017, Inf. Sci..
[13] Constantinos Panagiotakopoulos,et al. The Stochastic Gradient Descent for the Primal L1-SVM Optimization Revisited , 2013, ECML/PKDD.
[14] Francisco Herrera,et al. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms , 2011, Swarm Evol. Comput..
[15] M. Aizerman,et al. Theoretical Foundations of the Potential Function Method in Pattern Recognition Learning , 1964 .
[16] Léon Bottou,et al. Large-Scale Machine Learning with Stochastic Gradient Descent , 2010, COMPSTAT.
[17] Alexander J. Smola,et al. Large Margin Classification for Moving Targets , 2002, ALT.
[18] Vojislav Kecman,et al. Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models , 2001 .
[19] Samy Bengio,et al. Links between perceptrons, MLPs and SVMs , 2004, ICML.
[20] Vojislav Kecman,et al. Speeding up online training of L1 Support Vector Machines , 2016, SoutheastCon 2016.
[21] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[22] Stéphane Lecoeuche,et al. Application of an incremental SVM algorithm for on-line human recognition from video surveillance using texture and color features , 2014, Neurocomputing.
[23] Ralf Herbrich,et al. Learning Kernel Classifiers , 2001 .
[24] Haydemar Núñez,et al. Handling binary classification problems with a priority class by using Support Vector Machines , 2017, Appl. Soft Comput..
[25] Shinq-Jen Wu,et al. Two-phase optimization for support vectors and parameter selection of support vector machines: Two-class classification , 2017, Appl. Soft Comput..
[26] Richard Simon,et al. Bias in error estimation when using cross-validation for model selection , 2006, BMC Bioinformatics.
[27] Ljiljana Zigic,et al. Direct L2 Support Vector Machine , 2016 .
[28] Yoram Singer,et al. Pegasos: primal estimated sub-gradient solver for SVM , 2011, Math. Program..
[29] Bernhard E. Boser,et al. A training algorithm for optimal margin classifiers , 1992, COLT '92.
[30] Ivor W. Tsang,et al. Core Vector Machines: Fast SVM Training on Very Large Data Sets , 2005, J. Mach. Learn. Res..
[31] Kristin P. Bennett,et al. Duality and Geometry in SVM Classifiers , 2000, ICML.
[32] Bartosz Krawczyk,et al. Multidimensional data classification with chordal distance based kernel and Support Vector Machines , 2015, Eng. Appl. Artif. Intell..
[33] V. Kecman,et al. Iterative Single Data Algorithm for Training Kernel Machines from Huge Data Sets: Theory and Performance , 2005 .
[34] Léon Bottou,et al. The Tradeoffs of Large Scale Learning , 2007, NIPS.
[35] Bartosz Krawczyk,et al. Ensemble of Extreme Learning Machines with trained classifier combination and statistical features for hyperspectral data , 2018, Neurocomputing.
[36] Bartosz Krawczyk,et al. Tackling label noise with multi-class decomposition using fuzzy one-class support vector machines , 2016, 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).
[37] S. Sathiya Keerthi,et al. Improvements to Platt's SMO Algorithm for SVM Classifier Design , 2001, Neural Computation.
[38] Vojislav Kecman,et al. Geometric approach to support vector machines learning for large datasets , 2013 .
[39] Vojislav Kecman,et al. Fast online algorithms for Support Vector Machines , 2016, SoutheastCon 2016.