{"id":18015497,"url":"https://github.com/wmkouw/tcpr","last_synced_at":"2026-07-18T06:36:25.630Z","repository":{"id":89538308,"uuid":"50745853","full_name":"wmkouw/tcpr","owner":"wmkouw","description":"Target contrastive pessimistic risk minimization","archived":false,"fork":false,"pushed_at":"2022-01-10T14:12:02.000Z","size":920,"stargazers_count":0,"open_issues_count":0,"forks_count":1,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-04-04T15:17:13.049Z","etag":null,"topics":["domain-adaptation","machine-learning","risk-minimization","robust"],"latest_commit_sha":null,"homepage":"","language":"MATLAB","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/wmkouw.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,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2016-01-30T21:31:42.000Z","updated_at":"2022-01-10T14:12:05.000Z","dependencies_parsed_at":null,"dependency_job_id":"88d6b756-118e-496d-b0a2-91f0ff483f92","html_url":"https://github.com/wmkouw/tcpr","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/wmkouw/tcpr","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wmkouw%2Ftcpr","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wmkouw%2Ftcpr/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wmkouw%2Ftcpr/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wmkouw%2Ftcpr/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/wmkouw","download_url":"https://codeload.github.com/wmkouw/tcpr/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/wmkouw%2Ftcpr/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":35609253,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-05-26T15:22:16.424Z","status":"online","status_checked_at":"2026-07-18T02:00:07.223Z","response_time":61,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["domain-adaptation","machine-learning","risk-minimization","robust"],"created_at":"2024-10-30T04:14:04.546Z","updated_at":"2026-07-18T06:36:25.612Z","avatar_url":"https://github.com/wmkouw.png","language":"MATLAB","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Target contrastive pessimistic risk\r\n\r\nThis repository accompanies the paper:\r\n\r\n\"Robust domain-adaptive discriminant analysis\"\r\n\r\npublished in Pattern Recognition Letters, vol. 248, pp 107-113, 2021 ([doi](https://doi.org/10.1016/j.patrec.2021.05.005)). \r\n\r\n## Installation\r\n\r\nDownload:\r\n\r\n- Junfeng Wens's Robust Covariate Shift Adjustment: https://webdocs.cs.ualberta.ca/~jwen4/codes/RobustLearning.zip\r\n- Mark Schmidt's minFunc: http://www.cs.ubc.ca/~schmidtm/Software/minFunc.html\r\n\r\nAdd the unzipped folders to your path.\r\n\r\n## Usage\r\n\r\nEach folder marked __experiment\\*__, contains a script starting with __run_exp\\*__. It calls a function that contains experimental parameters, such as which classifiers to test, and runs the experiment. Results will be stored in a new folder. These can be gathered and printed by using the function __gather_exp\\*__.\r\n\r\n### Contact\r\n\r\nQuestions, bugs, and general feedback can be submitted to the [issues tracker](https://github.com/wmkouw/tcpr/issues).\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwmkouw%2Ftcpr","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwmkouw%2Ftcpr","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwmkouw%2Ftcpr/lists"}