文件名称:SparseBayes
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实现有效的学习算法 的稀疏贝叶斯模型,即稀疏贝叶斯matlab工具箱-"SparseBayes" is a package of Matlab functions designed to implement
an efficient learning algorithm for "Sparse Bayesian" models.
The "Version 2" package is an expanded implementation of the algorithm
detailed in:
Tipping, M. E. and A. C. Faul (2003). "Fast marginal likelihood
maximisation for sparse Bayesian models." In C. M. Bishop and
B. J. Frey (Eds.), Proceedings of the Ninth International Workshop
on Artificial Intelligence and Statistics, Key West, FL, Jan 3-6.
This paper, the accompanying code, and further information regarding
Sparse Bayesian models
an efficient learning algorithm for "Sparse Bayesian" models.
The "Version 2" package is an expanded implementation of the algorithm
detailed in:
Tipping, M. E. and A. C. Faul (2003). "Fast marginal likelihood
maximisation for sparse Bayesian models." In C. M. Bishop and
B. J. Frey (Eds.), Proceedings of the Ninth International Workshop
on Artificial Intelligence and Statistics, Key West, FL, Jan 3-6.
This paper, the accompanying code, and further information regarding
Sparse Bayesian models
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下载文件列表
SparseBayes\licence.txt
...........\Readme.txt
...........\SB2_ControlSettings.m
...........\SB2_Diagnostic.m
...........\SB2_FormatTime.m
...........\SB2_FullStatistics.m
...........\SB2_Initialisation.m
...........\SB2_Likelihoods.m
...........\SB2_Manual.pdf
...........\SB2_ParameterSettings.m
...........\SB2_PosteriorMode.m
...........\SB2_PreProcessBasis.m
...........\SB2_Sigmoid.m
...........\SB2_UserOptions.m
...........\SparseBayes.m
...........\SparseBayesDemo.m
SparseBayes
...........\Readme.txt
...........\SB2_ControlSettings.m
...........\SB2_Diagnostic.m
...........\SB2_FormatTime.m
...........\SB2_FullStatistics.m
...........\SB2_Initialisation.m
...........\SB2_Likelihoods.m
...........\SB2_Manual.pdf
...........\SB2_ParameterSettings.m
...........\SB2_PosteriorMode.m
...........\SB2_PreProcessBasis.m
...........\SB2_Sigmoid.m
...........\SB2_UserOptions.m
...........\SparseBayes.m
...........\SparseBayesDemo.m
SparseBayes