Shared domains of competence of approximate learning models using measures of separability of classes
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[1] Narasimhan Sundararajan,et al. Risk-sensitive loss functions for sparse multi-category classification problems , 2008, Inf. Sci..
[2] Tin Kam Ho,et al. Domain of competence of XCS classifier system in complexity measurement space , 2005, IEEE Transactions on Evolutionary Computation.
[3] Minrui Fei,et al. A fast multi-output RBF neural network construction method , 2010, Neurocomputing.
[4] James C. Bezdek,et al. Nearest prototype classifier designs: An experimental study , 2001, Int. J. Intell. Syst..
[5] Francisco Herrera,et al. A study on the use of statistical tests for experimentation with neural networks: Analysis of parametric test conditions and non-parametric tests , 2007, Expert Syst. Appl..
[6] Seppo J. Ovaska,et al. So near and yet so far: New insight into properties of some well-known classifier paradigms , 2010, Inf. Sci..
[7] Li Chunxia,et al. Task decomposition and modular single-hidden-layer perceptron classifiers for multi-class learning problems , 2007, Pattern Recognit..
[8] Martin Fodslette Meiller. A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning , 1993 .
[9] Renato De Leone,et al. Integrating support vector machines and neural networks , 2007, Neural Networks.
[10] Thomas Villmann,et al. Fuzzy classification using information theoretic learning vector quantization , 2008, Neurocomputing.
[11] Martin D. Buhmann,et al. Radial Basis Functions , 2021, Encyclopedia of Mathematical Geosciences.
[12] Jianjun Wang,et al. Margin calibration in SVM class-imbalanced learning , 2009, Neurocomputing.
[13] Kazuyuki Murase,et al. Faster Training Using Fusion of Activation Functions for Feed Forward Neural Networks , 2009, Int. J. Neural Syst..
[14] Ravi Kothari,et al. Feature subset selection using a new definition of classifiability , 2003, Pattern Recognit. Lett..
[15] José Ramón Cano,et al. Diagnose Effective Evolutionary Prototype Selection Using an Overlapping Measure , 2009, Int. J. Pattern Recognit. Artif. Intell..
[16] D. Broomhead,et al. Radial Basis Functions, Multi-Variable Functional Interpolation and Adaptive Networks , 1988 .
[17] Michael Biehl,et al. Performance analysis of LVQ algorithms: A statistical physics approach , 2006, Neural Networks.
[18] Thomas G. Dietterich. What is machine learning? , 2020, Archives of Disease in Childhood.
[19] Jue Wang,et al. Recursive Support Vector Machines for Dimensionality Reduction , 2008, IEEE Transactions on Neural Networks.
[20] Sameer Singh,et al. Multiresolution Estimates of Classification Complexity , 2003, IEEE Trans. Pattern Anal. Mach. Intell..
[21] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[22] Sammy Siu,et al. Analysis of the Initial Values in Split-Complex Backpropagation Algorithm , 2008, IEEE Transactions on Neural Networks.
[23] David S. Broomhead,et al. Multivariable Functional Interpolation and Adaptive Networks , 1988, Complex Syst..
[24] T. Ho,et al. Data Complexity in Pattern Recognition , 2006 .
[25] Zenglin Xu,et al. A novel kernel-based maximum a posteriori classification method , 2009, Neural Networks.
[26] John C. Platt,et al. Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .
[27] Carlos Renjifo,et al. Improving radial basis function kernel classification through incremental learning and automatic parameter selection , 2008, Neurocomputing.
[28] Alexandros Kalousis,et al. Algorithm selection via meta-learning , 2002 .
[29] José Martínez Sotoca,et al. An analysis of how training data complexity affects the nearest neighbor classifiers , 2007, Pattern Analysis and Applications.
[30] Tin Kam Ho,et al. Complexity Measures of Supervised Classification Problems , 2002, IEEE Trans. Pattern Anal. Mach. Intell..
[31] Richard Baumgartner,et al. Data complexity assessment in undersampled classification of high-dimensional biomedical data , 2006, Pattern Recognit. Lett..
[33] B. John Oommen,et al. On using prototype reduction schemes to enhance the computation of volume-based inter-class overlap measures , 2009, Pattern Recognit..
[34] FRED W. SMITH,et al. Pattern Classifier Design by Linear Programming , 1968, IEEE Transactions on Computers.
[35] Mao Yi-bo. Algorithm for Approximation Order of Multiscaling Function , 2004 .
[36] Ricardo Vilalta,et al. Metalearning - Applications to Data Mining , 2008, Cognitive Technologies.
[37] José Martínez Sotoca,et al. Data Characterization for Effective Prototype Selection , 2005, IbPRIA.
[38] Francisco Herrera,et al. Domains of competence of fuzzy rule based classification systems with data complexity measures: A case of study using a fuzzy hybrid genetic based machine learning method , 2010, Fuzzy Sets Syst..
[39] Ming Dong,et al. Classifiability based omnivariate decision trees , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..
[40] Richard Weber,et al. Simultaneous feature selection and classification using kernel-penalized support vector machines , 2011, Inf. Sci..
[41] Raúl Rojas,et al. Neural Networks - A Systematic Introduction , 1996 .
[42] Yen-Jen Oyang,et al. Data classification with radial basis function networks based on a novel kernel density estimation algorithm , 2005, IEEE Transactions on Neural Networks.
[43] Martin D. Buhmann,et al. Radial Basis Functions: Theory and Implementations: Preface , 2003 .