Building Interpretable Systems in Real Time
暂无分享,去创建一个
[1] Hisao Ishibuchi,et al. Analysis of interpretability-accuracy tradeoff of fuzzy systems by multiobjective fuzzy genetics-based machine learning , 2007, Int. J. Approx. Reason..
[2] Jesús Alcalá-Fdez,et al. Genetic learning of accurate and compact fuzzy rule based systems based on the 2-tuples linguistic representation , 2007, Int. J. Approx. Reason..
[3] Satinder P. Singh,et al. Kernel Predictive Linear Gaussian models for nonlinear stochastic dynamical systems , 2006, ICML.
[4] Jason Weston,et al. Fast Kernel Classifiers with Online and Active Learning , 2005, J. Mach. Learn. Res..
[5] Shie Mannor,et al. The kernel recursive least-squares algorithm , 2004, IEEE Transactions on Signal Processing.
[6] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[7] Nikola K. Kasabov,et al. DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction , 2002, IEEE Trans. Fuzzy Syst..
[8] Giovanna Castellano,et al. Distinguishability quantification of fuzzy sets , 2007, Inf. Sci..
[9] Ignacio Santamaría,et al. A Sliding-Window Kernel RLS Algorithm and Its Application to Nonlinear Channel Identification , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.
[10] Bernard De Baets,et al. Interpretability-preserving genetic optimization of linguistic terms in fuzzy models for fuzzy ordered classification: An ecological case study , 2007, Int. J. Approx. Reason..
[11] Oliver Nelles,et al. LOLIMOT - Lokale, lineare Modelle zur Identifikation nichtlinearer, dynamischer Systeme , 1997 .
[12] Héctor Pomares,et al. Improving the accuracy while preserving the interpretability of fuzzy function approximators by means of multi-objective evolutionary algorithms , 2007, Int. J. Approx. Reason..
[13] Anukool Lakhina,et al. Multivariate Online Anomaly Detection Using Kernel Recursive Least Squares , 2007, IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications.
[14] Uzay Kaymak,et al. Similarity measures in fuzzy rule base simplification , 1998, IEEE Trans. Syst. Man Cybern. Part B.
[15] Ralf Mikut,et al. Interpretability issues in data-based learning of fuzzy systems , 2005, Fuzzy Sets Syst..
[16] Ronald R. Yager,et al. Learning of Fuzzy Rules by Mountain Clustering , 1992 .
[17] J. Casillas. Interpretability issues in fuzzy modeling , 2003 .
[18] A. Dourado,et al. On the complexity and interpretability of support vector machines for process modeling , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).
[19] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[20] Yaochu Jin,et al. Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement , 2000, IEEE Trans. Fuzzy Syst..
[21] D.P. Filev,et al. An approach to online identification of Takagi-Sugeno fuzzy models , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[22] Jyh-Shing Roger Jang,et al. ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..
[23] Feng Liu,et al. A Novel Generic Hebbian Ordering-Based Fuzzy Rule Base Reduction Approach to Mamdani Neuro-Fuzzy System , 2007, Neural Computation.
[24] Daniel A. Keim,et al. Information Visualization and Visual Data Mining , 2002, IEEE Trans. Vis. Comput. Graph..
[25] Antonio F. Gómez-Skarmeta,et al. Improving interpretability in approximative fuzzy models via multiobjective evolutionary algorithms , 2007, EUSFLAT Conf..
[26] Plamen P. Angelov,et al. Data-driven evolving fuzzy systems using eTS and FLEXFIS: comparative analysis , 2008, Int. J. Gen. Syst..
[27] Bernhard Sendhoff,et al. Extracting Interpretable Fuzzy Rules from RBF Networks , 2003, Neural Processing Letters.
[28] Plamen P. Angelov,et al. Fuzzy systems design: direct and indirect approaches , 2006, Soft Comput..
[29] John Q. Gan,et al. Constructing accurate and parsimonious fuzzy models with distinguishable fuzzy sets based on an entropy measure , 2006, Fuzzy Sets Syst..