Statistical Learning and Data Sciences
暂无分享,去创建一个
Harris Papadopoulos | Alexander Gammerman | Vladimir Vovk | A. Gammerman | V. Vovk | H. Papadopoulos
[1] Plamen Angelov,et al. Evolving Intelligent Systems: Methodology and Applications , 2010 .
[2] Plamen Angelov,et al. Anomaly detection based on eccentricity analysis , 2014, 2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS).
[3] Gert Cauwenberghs,et al. Incremental and Decremental Support Vector Machine Learning , 2000, NIPS.
[4] Subhransu Maji,et al. Classification using intersection kernel support vector machines is efficient , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[5] F. Klawonn,et al. Evolving Fuzzy Rule-based Classifiers , 2007, 2007 IEEE Symposium on Computational Intelligence in Image and Signal Processing.
[6] Simei Gomes Wysoski,et al. Adaptive Learning Procedure for a Network of Spiking Neurons and Visual Pattern Recognition , 2006, ACIVS.
[7] Plamen Angelov,et al. Outside the box: an alternative data analytics framework , 2014, J. Autom. Mob. Robotics Intell. Syst..
[8] K M Søndergaard,et al. [Understanding statistics?]. , 1995, Ugeskrift for laeger.
[9] Bernhard E. Boser,et al. A training algorithm for optimal margin classifiers , 1992, COLT '92.
[10] 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.
[11] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[12] Arnaud Delorme,et al. Spike-based strategies for rapid processing , 2001, Neural Networks.
[13] Stefan Schliebs,et al. Evolving spiking neural network—a survey , 2013, Evolving Systems.
[14] Nanning Zheng,et al. Survival kernel with application to kernel adaptive filtering , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).
[15] P. Mahalanobis. On the generalized distance in statistics , 1936 .
[16] Liping Han,et al. Distance Weighted Cosine Similarity Measure for Text Classification , 2013, IDEAL.
[17] Thomas S. Huang,et al. One-class SVM for learning in image retrieval , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).
[18] Chih-Jen Lin,et al. Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel , 2003, Neural Computation.
[19] Siti Zaiton Mohd Hashim,et al. Training feedforward neural networks using hybrid particle swarm optimization and gravitational search algorithm , 2012, Appl. Math. Comput..
[20] Nikola Kasabov,et al. Evolving Connectionist Systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines , 2002, IEEE Transactions on Neural Networks.
[21] 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).
[22] Plamen Angelov,et al. Autonomous Learning Systems: From Data Streams to Knowledge in Real-time , 2013 .
[23] Michael Defoin-Platel,et al. Integrated Feature and Parameter Optimization for an Evolving Spiking Neural Network , 2008, ICONIP.
[24] V. Vapnik. Pattern recognition using generalized portrait method , 1963 .
[25] 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..
[26] Jacques Gautrais,et al. Rank order coding , 1998 .
[27] Arnaud Delorme,et al. Networks of integrate-and-fire neurons using Rank Order Coding B: Spike timing dependent plasticity and emergence of orientation selectivity , 2001, Neurocomputing.
[28] Plamen P. Angelov,et al. A new type of simplified fuzzy rule-based system , 2012, Int. J. Gen. Syst..
[29] Thomas Hofmann,et al. Support vector machine learning for interdependent and structured output spaces , 2004, ICML.
[30] Bernhard Schölkopf,et al. New Support Vector Algorithms , 2000, Neural Computation.