Generalized Approximate Cross Validation For Support Vector Machines, Or, Another Way To Look At Mar
暂无分享,去创建一个
[1] Xiwu Lin,et al. Smoothing spline ANOVA models for large data sets with Bernoulli observations and the randomized GACV , 2000 .
[2] G. Wahba. Support vector machines, reproducing kernel Hilbert spaces, and randomized GACV , 1999 .
[3] B. Schölkopf,et al. Advances in kernel methods: support vector learning , 1999 .
[4] Nello Cristianini,et al. Advances in Kernel Methods - Support Vector Learning , 1999 .
[5] Nello Cristianini,et al. Dynamically Adapting Kernels in Support Vector Machines , 1998, NIPS.
[6] Tomaso A. Poggio,et al. A Sparse Representation for Function Approximation , 1998, Neural Computation.
[7] Federico Girosi,et al. An Equivalence Between Sparse Approximation and Support Vector Machines , 1998, Neural Computation.
[8] J. C. BurgesChristopher. A Tutorial on Support Vector Machines for Pattern Recognition , 1998 .
[9] P. Bartlett,et al. Generalization Performance of Support Vector Machines and Other Pattern Classifiers , 1999 .
[10] R. Tibshirani,et al. Improvements on Cross-Validation: The 632+ Bootstrap Method , 1997 .
[11] Thomas G. Dietterich. What is machine learning? , 2020, Archives of Disease in Childhood.
[12] Dana Ron,et al. An Experimental and Theoretical Comparison of Model Selection Methods , 1995, COLT '95.
[13] G. Wahba. Spline Models for Observational Data , 1990 .
[14] Grace Wahba,et al. Constrained Regularization for Ill Posed Linear Operator Equations, with Applications in Meteorology and Medicine. , 1982 .