{"action":"created","issue":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/1513","labels_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/1513/labels{/name}","comments_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/1513/comments","events_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/1513/events","html_url":"https://github.com/scikit-learn/scikit-learn/issues/1513","id":9657490,"number":1513,"title":" Relevance Vector Machine (RVM)","user":{"login":"yedtoss","id":1477156,"avatar_url":"https://avatars.githubusercontent.com/u/1477156?v=3","gravatar_id":"","url":"https://api.github.com/users/yedtoss","html_url":"https://github.com/yedtoss","followers_url":"https://api.github.com/users/yedtoss/followers","following_url":"https://api.github.com/users/yedtoss/following{/other_user}","gists_url":"https://api.github.com/users/yedtoss/gists{/gist_id}","starred_url":"https://api.github.com/users/yedtoss/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/yedtoss/subscriptions","organizations_url":"https://api.github.com/users/yedtoss/orgs","repos_url":"https://api.github.com/users/yedtoss/repos","events_url":"https://api.github.com/users/yedtoss/events{/privacy}","received_events_url":"https://api.github.com/users/yedtoss/received_events","type":"User","site_admin":false},"labels":[{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/labels/New%20Feature","name":"New Feature","color":"02e10c"}],"state":"closed","locked":false,"assignee":null,"milestone":null,"comments":41,"created_at":"2013-01-03T21:55:27Z","updated_at":"2015-10-15T04:56:58Z","closed_at":"2013-03-19T14:58:47Z","body":"RVM is a Bayesian framework for obtaining sparse solutions to regression and classification tasks. It used a model of identical form to SVM ( Support Vector Machine). It solves the following disadvantages of SVM:\r\n-- The number of basis function in SVM grows linearly with the size of the training set\r\n       In RVM, we start with 0 basis and incrementally update (add/delete) the set of basis function until convergence. \r\n\r\n--  SVM predictions are not probabilistic while RVM's are probabilistic\r\n\r\n-- It is necessary in SVM to estimate the margin trade-off parameter 'C' which is not the case in RVM\r\n\r\n-- SVM kernel should be positive definite. In RVM we can use any kernel.\r\n\r\nIt is already implemented in dlib  http://dlib.net/dlib/svm/rvm_abstract.h.html  and there is also a matlab implementation here http://www.vectoranomaly.com/downloads/downloads.htm.  These codes should serve as a guide.\r\nI think it will be a good idea to add it to scikit-learn.\r\n\r\nReferences : \r\n1- `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. `\r\n\r\n2- `Tipping, M. E. (2001). Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning Research  1, 211–244.`"},"comment":{"url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/comments/148281784","html_url":"https://github.com/scikit-learn/scikit-learn/issues/1513#issuecomment-148281784","issue_url":"https://api.github.com/repos/scikit-learn/scikit-learn/issues/1513","id":148281784,"user":{"login":"GaelVaroquaux","id":208217,"avatar_url":"https://avatars.githubusercontent.com/u/208217?v=3","gravatar_id":"","url":"https://api.github.com/users/GaelVaroquaux","html_url":"https://github.com/GaelVaroquaux","followers_url":"https://api.github.com/users/GaelVaroquaux/followers","following_url":"https://api.github.com/users/GaelVaroquaux/following{/other_user}","gists_url":"https://api.github.com/users/GaelVaroquaux/gists{/gist_id}","starred_url":"https://api.github.com/users/GaelVaroquaux/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/GaelVaroquaux/subscriptions","organizations_url":"https://api.github.com/users/GaelVaroquaux/orgs","repos_url":"https://api.github.com/users/GaelVaroquaux/repos","events_url":"https://api.github.com/users/GaelVaroquaux/events{/privacy}","received_events_url":"https://api.github.com/users/GaelVaroquaux/received_events","type":"User","site_admin":false},"created_at":"2015-10-15T04:56:58Z","updated_at":"2015-10-15T04:56:58Z","body":"> @amueller RVM (mainly revelance vector regression (RVR)) is pretty useful in\n> neuroimaging data analysis.\n\nI am not convinced (and I do neuroimaging). I haven't seen a good\nempirical comparison.\n\nI see the way to go is to have RVMs in a separate package, with\nscikit-learn API, and encourage good empirical work to show their\nusefulness. If they are useful, merge them in scikit-learn.\n"}}