A dynamic optimization approach for adaptive incremental learning
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[1] Vasant Honavar,et al. Learn++: an incremental learning algorithm for supervised neural networks , 2001, IEEE Trans. Syst. Man Cybern. Part C.
[2] C. A. Murthy,et al. Data condensation in large databases by incremental learning with support vector machines , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.
[3] Sayan Mukherjee,et al. Choosing Multiple Parameters for Support Vector Machines , 2002, Machine Learning.
[4] Mykola Pechenizkiy,et al. Dynamic integration of classifiers for handling concept drift , 2008, Inf. Fusion.
[5] Philip S. Yu,et al. Mining concept-drifting data streams using ensemble classifiers , 2003, KDD '03.
[6] Chih-Jen Lin,et al. Probability Estimates for Multi-class Classification by Pairwise Coupling , 2003, J. Mach. Learn. Res..
[7] Huan Liu,et al. Handling concept drifts in incremental learning with support vector machines , 1999, KDD '99.
[8] James Kennedy,et al. Some Issues and Practices for Particle Swarms , 2007, 2007 IEEE Swarm Intelligence Symposium.
[9] Shaoning Pang,et al. Incremental Learning of Chunk Data for Online Pattern Classification Systems , 2008, IEEE Transactions on Neural Networks.
[10] Dimitrios Gunopulos,et al. Incremental support vector machine construction , 2001, Proceedings 2001 IEEE International Conference on Data Mining.
[11] Leo Breiman,et al. Pasting Small Votes for Classification in Large Databases and On-Line , 1999, Machine Learning.
[12] Ludmila I. Kuncheva,et al. Classifier Ensembles for Changing Environments , 2004, Multiple Classifier Systems.
[13] Abraham Kandel,et al. Real-time data mining of non-stationary data streams from sensor networks , 2008, Inf. Fusion.
[14] Ethem Alpaydin,et al. Incremental construction of classifier and discriminant ensembles , 2009, Inf. Sci..
[15] Geoff Hulten,et al. Mining time-changing data streams , 2001, KDD '01.
[16] Padraig Cunningham,et al. A case-based technique for tracking concept drift in spam filtering , 2004, Knowl. Based Syst..
[17] Maurice Clerc,et al. The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..
[18] Gert Cauwenberghs,et al. SVM incremental learning, adaptation and optimization , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..
[19] Mohammad Mehdi Ebadzadeh,et al. DNPSO: A Dynamic Niching Particle Swarm Optimizer for multi-modal optimization , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).
[20] A. Carlisle,et al. Tracking changing extrema with adaptive particle swarm optimizer , 2002, Proceedings of the 5th Biannual World Automation Congress.
[21] Russell C. Eberhart,et al. Adaptive particle swarm optimization: detection and response to dynamic systems , 2002, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600).
[22] Robert Sabourin,et al. An empirical study on diversity measures and margin theory for ensembles of classifiers , 2007, 2007 10th International Conference on Information Fusion.
[23] Kenneth O. Stanley. Learning Concept Drift with a Committee of Decision Trees , 2003 .
[24] Robi Polikar,et al. An Ensemble-Based Incremental Learning Approach to Data Fusion , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[25] Johannes Gehrke,et al. Mining data streams under block evolution , 2002, SKDD.
[26] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[27] Robert Sabourin,et al. A PSO-based framework for dynamic SVM model selection , 2009, GECCO '09.
[28] Stephen Grossberg,et al. Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps , 1992, IEEE Trans. Neural Networks.
[29] Niall M. Adams,et al. The impact of changing populations on classifier performance , 1999, KDD '99.
[30] Chih-Jen Lin,et al. LIBSVM: A library for support vector machines , 2011, TIST.
[31] Marimuthu Palaniswami,et al. Incremental training of support vector machines , 2005, IEEE Transactions on Neural Networks.
[32] Jin-Long An,et al. An incremental learning algorithm for support vector machine , 2003, Proceedings of the 2003 International Conference on Machine Learning and Cybernetics (IEEE Cat. No.03EX693).
[33] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[34] Riccardo Poli,et al. Particle swarm optimization , 1995, Swarm Intelligence.
[35] Giorgio Valentini,et al. An experimental bias-variance analysis of SVM ensembles based on resampling techniques , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).