Relevance-Vector-Machine

代码说明：

说明：  相关向量机（Relevance Vector Machine，简称RVM）是Micnacl E.Tipping于2000年提出的一种与SVM（Support Vector Machine）类似的稀疏概率模型，是一种新的监督学习方法。 它的训练是在贝叶斯框架下进行的，在先验参数的结构下基于主动相关决策理论（automatic relevance determination，简称ARD）来移除不相关的点，从而获得稀疏化的模型。在样本数据的迭代学习过程中，大部分参数的后验分布趋于零，与预测值无关，那些非零参数对应的点被称作相关向量（Relevance Vectors），体现了数据中最核心的特征。同支持向量机相比，相关向量机最大的优点就是极大地减少了核函数的计算量，并且也克服了所选核函数必须满足Mercer条件的缺点。(Relevance Vector Machine (RVM) is a sparse probability model similar to SVM (Support Vector Machine) proposed by Micnacl E. Tipping in 2000. It is a new supervised learning method. Its training is carried out under the Bayesian framework. Under the structure of prior parameters, it is based on Automatic Relevance Determination (ARD) to remove the irrelevant points, so as to obtain the sparse model. In the iterative learning process of sample data, the posterior distribution of most parameters tends to zero, which is independent of the predicted value. The points corresponding to non-zero parameters are called Relevance Vectors, which represent the most core features of the data. Compared with support vector machine, the biggest advantage of correlation vector machine is that it greatly reduces the computation amount of kernel function, and also overcomes the shortcoming that the selected kernel function must meet Mercer's condition.)

文件列表：

Relevance-Vector-Machine\demo.m, 811 , 2019-07-05
Relevance-Vector-Machine\func\computeKM.m, 540 , 2019-07-05
Relevance-Vector-Machine\func\computePretIndex.m, 637 , 2019-07-05
Relevance-Vector-Machine\func\generateData.m, 631 , 2019-07-05
Relevance-Vector-Machine\func\plottestingResult.m, 1327 , 2019-07-05
Relevance-Vector-Machine\func\plottrainingResult.m, 1222 , 2019-07-05
Relevance-Vector-Machine\func\rvm_test.m, 989 , 2019-07-05
Relevance-Vector-Machine\func\rvm_train.m, 2353 , 2019-07-05
Relevance-Vector-Machine\img\img1.png, 27149 , 2019-07-05
Relevance-Vector-Machine\img\img2.png, 46519 , 2019-07-05
Relevance-Vector-Machine\README.md, 1087 , 2019-07-05
Relevance-Vector-Machine\refs\SB2_Manual.pdf, 133380 , 2019-07-05
Relevance-Vector-Machine\refs\Tipping_2001_Sparse Bayesian learning and the relevance vector machine.pdf, 958100 , 2019-07-05
Relevance-Vector-Machine\refs\Tipping_Faul_2003_Fast marginal likelihood maximisation for sparse Bayesian models.pdf, 228209 , 2019-07-05
Relevance-Vector-Machine\SB2_Release_200\licence.txt, 15122 , 2019-07-05
Relevance-Vector-Machine\SB2_Release_200\Readme.txt, 2649 , 2019-07-05
Relevance-Vector-Machine\SB2_Release_200\SB2_ControlSettings.m, 4426 , 2019-07-05
Relevance-Vector-Machine\SB2_Release_200\SB2_Diagnostic.m, 3714 , 2019-07-05
Relevance-Vector-Machine\SB2_Release_200\SB2_FormatTime.m, 1579 , 2019-07-05
Relevance-Vector-Machine\SB2_Release_200\SB2_FullStatistics.m, 5816 , 2019-07-05
Relevance-Vector-Machine\SB2_Release_200\SB2_Initialisation.m, 7221 , 2019-07-05
Relevance-Vector-Machine\SB2_Release_200\SB2_Likelihoods.m, 2161 , 2019-07-05
Relevance-Vector-Machine\SB2_Release_200\SB2_Manual.pdf, 133380 , 2019-07-05
Relevance-Vector-Machine\SB2_Release_200\SB2_ParameterSettings.m, 3053 , 2019-07-05
Relevance-Vector-Machine\SB2_Release_200\SB2_PosteriorMode.m, 6200 , 2019-07-05
Relevance-Vector-Machine\SB2_Release_200\SB2_PreProcessBasis.m, 1835 , 2019-07-05
Relevance-Vector-Machine\SB2_Release_200\SB2_Sigmoid.m, 1113 , 2019-07-05
Relevance-Vector-Machine\SB2_Release_200\SB2_UserOptions.m, 5715 , 2019-07-05
Relevance-Vector-Machine\SB2_Release_200\SparseBayes.m, 24447 , 2019-07-05
Relevance-Vector-Machine\SB2_Release_200\SparseBayesDemo.m, 9463 , 2019-07-05
Relevance-Vector-Machine\func, 0 , 2020-05-13
Relevance-Vector-Machine\img, 0 , 2020-05-13
Relevance-Vector-Machine\refs, 0 , 2020-05-13
Relevance-Vector-Machine\SB2_Release_200, 0 , 2020-05-13
Relevance-Vector-Machine, 0 , 2020-05-13