{"id":13482163,"url":"https://github.com/siavashserver/neonrvm","last_synced_at":"2025-03-27T12:32:21.883Z","repository":{"id":57445466,"uuid":"121837287","full_name":"siavashserver/neonrvm","owner":"siavashserver","description":"An open source machine learning library for performing regression tasks using RVM technique.","archived":true,"fork":false,"pushed_at":"2020-08-23T14:17:53.000Z","size":1203,"stargazers_count":40,"open_issues_count":0,"forks_count":5,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-03-10T18:12:29.154Z","etag":null,"topics":["bayesian","bindings","c","machine-learning","python","regression","relevance-vector-machine","rvm","sparse"],"latest_commit_sha":null,"homepage":"https://siavashserver.github.io/neonrvm/","language":"C","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/siavashserver.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2018-02-17T07:50:47.000Z","updated_at":"2025-02-20T18:48:47.000Z","dependencies_parsed_at":"2022-09-13T03:10:47.682Z","dependency_job_id":null,"html_url":"https://github.com/siavashserver/neonrvm","commit_stats":null,"previous_names":[],"tags_count":3,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/siavashserver%2Fneonrvm","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/siavashserver%2Fneonrvm/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/siavashserver%2Fneonrvm/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/siavashserver%2Fneonrvm/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/siavashserver","download_url":"https://codeload.github.com/siavashserver/neonrvm/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245845280,"owners_count":20681880,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bayesian","bindings","c","machine-learning","python","regression","relevance-vector-machine","rvm","sparse"],"created_at":"2024-07-31T17:00:59.567Z","updated_at":"2025-03-27T12:32:21.490Z","avatar_url":"https://github.com/siavashserver.png","language":"C","funding_links":[],"categories":["C","C语言","Uncategorized"],"sub_categories":["Tools","[Tools](#tools-1)","Uncategorized"],"readme":"\u003cp align=\"center\"\u003e\r\n\u003cimg src=\"https://siavashserver.github.io/neonrvm/neonrvm.svg\" alt=\"neonrvm_logo\" title=\"neonrvm\"\u003e\r\n\u003c/p\u003e\r\n\r\n![GitHub](https://img.shields.io/github/license/siavashserver/neonrvm?style=flat-square)\r\n![GitHub release (latest SemVer including pre-releases)](https://img.shields.io/github/v/release/siavashserver/neonrvm?include_prereleases\u0026style=flat-square)\r\n![GitHub stars](https://img.shields.io/github/stars/siavashserver/neonrvm?style=flat-square)\r\n![PyPI - Status](https://img.shields.io/pypi/status/neonrvm?style=flat-square)\r\n\r\n# Introduction\r\n\r\n**neonrvm** is an open source machine learning library for performing regression\r\ntasks using [RVM] technique. It is written in C programming language and comes\r\nwith bindings for the Python programming language.\r\n\r\nneonrvm was born during my master's thesis to help reduce training times and\r\nrequired system resources. neonrvm did that by getting rid of multiple\r\nmiddleware layers and optimizing memory usage.\r\n\r\nUnder the hood neonrvm uses expectation maximization fitting method, and allows\r\nbasis functions to be fed incrementally to the model. This helps to keep\r\ntraining times and memory requirements significantly lower for large data sets.\r\n\r\nneonrvm is not trying to be a full featured machine learning framework, and only\r\nprovides core training and prediction facilities. You might want to use it in\r\nconjunction with higher level scientific programming languages and machine\r\nlearning tool kits instead.\r\n\r\nRVM technique is very sensitive to input data representation and kernel\r\nselection. You might consider something else if you are looking for a less\r\nchallenging solution.\r\n\r\n[RVM]: https://en.wikipedia.org/wiki/Relevance_vector_machine\r\n\r\n---\r\n\r\n# Documentation\r\n\r\nPlease visit the dedicated users guide page:\r\n[https://siavashserver.github.io/neonrvm/](https://siavashserver.github.io/neonrvm/)\r\n\r\n---\r\n\r\n# License\r\n\r\n- neonrvm is licensed under the [MIT] license. Please see `LICENSE` for more\r\n    details.\r\n\r\n- neonrvm includes code from [Netlib LAPACK] library, which is licensed under a\r\n    [modified BSD license].\r\n\r\n- The relevance vector machine is [patented] in the United States by\r\n  [Microsoft].\r\n\r\n[MIT]: https://en.wikipedia.org/wiki/MIT_License\r\n[Netlib LAPACK]: http://www.netlib.org/lapack/\r\n[modified BSD license]: http://www.netlib.org/lapack/LICENSE.txt\r\n[patented]: https://patents.google.com/patent/US6633857\r\n[Microsoft]: https://www.microsoft.com\r\n\r\n---\r\n\r\n# Future work\r\n\r\n- Investigate methods to make learning process numerically more stable\r\n- Implement classification\r\n- Create higher level wrappers and programming language bindings\r\n- Improve documentation\r\n\r\n---\r\n\r\n# Reference\r\n\r\n- Tipping, M. E. (2001). Sparse Bayesian learning and the relevance vector machine. Journal of machine learning research, 1(Jun), 211-244.\r\n- Ben-Shimon, D., \u0026 Shmilovici, A. (2006). Accelerating the relevance vector machine via data partitioning. Foundations of Computing and Decision Sciences, 31(1), 27-42.\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsiavashserver%2Fneonrvm","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsiavashserver%2Fneonrvm","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsiavashserver%2Fneonrvm/lists"}