Description: Relevance Vector Machine (RVM) of the matlab source code, including the fast algorithm that contains the code instructions. RVM to support vector machines with the same function form of sparse probabilistic model to predict the unknown function, or classification. Advantages: (1) The goal is not only the amount of the output forecast point estimates, but also the distribution of the output forecast. (2) use less number of support vectors, thus significantly reduce the amount of predictive value of the output goal of computing time. (3) RVM does not require too many parameters estimated. (4) RVM on whether to satisfy Mercer' s theorem is no limit on nuclear function, adaptability and better.
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- [rvm] - This is a perfect RVM algorithm. Good pe
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File list (Check if you may need any files):
SB2_Release_200\SB2_Release_200\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
...............\SB2_Release_200
SB2_Release_200
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