文件名称:RVM_matlabToolBox
- 所属分类:
- 人工智能/神经网络/遗传算法
- 资源属性:
- [Matlab] [源码]
- 上传时间:
- 2012-11-26
- 文件大小:
- 154kb
- 下载次数:
- 2次
- 提 供 者:
- 何**
- 相关连接:
- 无
- 下载说明:
- 别用迅雷下载,失败请重下,重下不扣分!
介绍说明--下载内容均来自于网络,请自行研究使用
相关向量机(RVM)的matlab源程序,包含快速算法,内含代码使用说明。
RVM采取是与支持向量机相同的函数形式稀疏概率模型,对未知函数进行预测或分类。 优点:
(1) 不仅仅输出预测目标量的点估计值,还可以输出预测值的分布.
(2) 使用更少数量的支持向量,从而显著减少输出目标量预测值的计算时间.
(3) RVM不需要估计过多的参数.
(4) RVM对是否满足Mercer 定理的核函数没有限制,适应性更好.
-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.
RVM采取是与支持向量机相同的函数形式稀疏概率模型,对未知函数进行预测或分类。 优点:
(1) 不仅仅输出预测目标量的点估计值,还可以输出预测值的分布.
(2) 使用更少数量的支持向量,从而显著减少输出目标量预测值的计算时间.
(3) RVM不需要估计过多的参数.
(4) RVM对是否满足Mercer 定理的核函数没有限制,适应性更好.
-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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支持向量机
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RVM
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RVM_matlabToolBox
支持向量机
source
RVM
with
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RVM
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(系统自动生成,下载前可以参看下载内容)
下载文件列表
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
...............\...............\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