Recursive SVM Based on TEDA
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[1] Thomas S. Huang,et al. One-class SVM for learning in image retrieval , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).
[2] N. Munoz Ceballos,et al. Simulation and Assessment Educational Framework for Mobile Robot Algorithms , 2014, J. Autom. Mob. Robotics Intell. Syst..
[3] Thomas Hofmann,et al. Support vector machine learning for interdependent and structured output spaces , 2004, ICML.
[4] Gert Cauwenberghs,et al. Incremental and Decremental Support Vector Machine Learning , 2000, NIPS.
[5] A. Skowron,et al. Methodology and applications , 1998 .
[6] Nasser M. Nasrabadi,et al. Pattern Recognition and Machine Learning , 2006, Technometrics.
[7] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[8] Andrew McCallum,et al. Dynamic conditional random fields: factorized probabilistic models for labeling and segmenting sequence data , 2004, J. Mach. Learn. Res..
[9] F. Klawonn,et al. Evolving Fuzzy Rule-based Classifiers , 2007, 2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing.
[10] Antonio Torralba,et al. Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.
[11] Plamen P. Angelov,et al. Evolving Classifier TEDAClass for Big Data , 2015, INNS Conference on Big Data.
[12] P. Mahalanobis. On the generalized distance in statistics , 1936 .
[13] Badong Chen,et al. Multiple adaptive kernel size KLMS for Beijing PM2.5 prediction , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).
[14] Fei-Fei Li,et al. What, where and who? Classifying events by scene and object recognition , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[15] Jianhua Yang,et al. Support vector clustering through proximity graph modelling , 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..
[16] Subhransu Maji,et al. Classification using intersection kernel support vector machines is efficient , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[17] Plamen Angelov,et al. Autonomous Learning Systems: From Data Streams to Knowledge in Real-time , 2013 .
[18] Plamen Angelov,et al. Evolving Intelligent Systems: Methodology and Applications , 2010 .
[19] Liping Han,et al. Distance Weighted Cosine Similarity Measure for Text Classification , 2013, IDEAL.
[20] Nanning Zheng,et al. Survival kernel with application to kernel adaptive filtering , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).
[21] Bernhard Schölkopf,et al. New Support Vector Algorithms , 2000, Neural Computation.
[22] Bernhard E. Boser,et al. A training algorithm for optimal margin classifiers , 1992, COLT '92.
[23] V. Vapnik. Pattern recognition using generalized portrait method , 1963 .
[24] Plamen P. Angelov,et al. A new type of simplified fuzzy rule-based system , 2012, Int. J. Gen. Syst..
[25] Plamen Angelov,et al. Autonomously evolving classifier TEDAClass , 2016, Inf. Sci..
[26] Muhaini Othman,et al. Evolving spiking neural networks for personalised modelling, classification and prediction of spatio-temporal patterns with a case study on stroke , 2014, Neurocomputing.
[27] Plamen Angelov,et al. Anomaly detection based on eccentricity analysis , 2014, 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS).
[28] Plamen P. Angelov,et al. Symbol recognition with a new autonomously evolving classifier autoclass , 2014, 2014 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS).
[29] Chih-Jen Lin,et al. Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel , 2003, Neural Computation.
[30] Plamen Angelov,et al. Outside the box: an alternative data analytics framework , 2014, J. Autom. Mob. Robotics Intell. Syst..