{"doi":"10.1109/ccpr.2010.5659293","title":"Support Vector Regression with Automatic Margin Control","abstract":null,"journal":"2010 Chinese Conference on Pattern Recognition (CCPR)","year":2010,"id":628879,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":897208,"name":"Jian Yang","orcid":"0000-0003-3281-8803","position":1,"is_corresponding":false},{"id":804123,"name":"Jun Liang","orcid":"0000-0002-4303-7669","position":2,"is_corresponding":false},{"id":1628460,"name":"Qiaolin Ye","orcid":null,"position":3,"is_corresponding":false},{"id":190890,"name":"Xiaobo Chen","orcid":"0000-0001-8755-6199","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Support Vector Regression with Automatic Margin Control","abstract":"Support vector regression (SVR) is a typical regression method, and has been successfully applied in many practical problems such as financial engineering. However, the conventional SVR depends mainly on the size of ε-insensitive margin which is unsuitable especially when samples are volatile and noisy. In this paper, we proposed a novel regression algorithm, termed as v-support vector regression with automatic margin control (AMC-v-SVR), to tackle this problem. AMC-v-SVR seeks the regressor and its up- and down- margins simultaneously by solving a single quadratic programming problem. The proposed regression algorithms have the advantage when the margin is not fixed and asymmetrical. Experimental results show the feasibility and effectiveness of the proposed method.","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19767382","pmcid":null,"openalex_id":"https://openalex.org/W2040975418","authors":[],"funders":[],"total_grants":0,"fwci":0.0,"citation_percentile":0.14949513,"influential_citations":0,"citation_trend":[],"oa_status":"closed","license":null,"oa_locations":[{"url":"http://xplorestaging.ieee.org/ielx5/5654671/5659117/05659293.pdf?arnumber=5659293","host_type":"publisher"},{"url":"https://doi.org/10.1109/ccpr.2010.5659293","host_type":""}],"fields_of_study":["Face and Expression Recognition","Neural Networks and Applications","Blind Source Separation Techniques"],"mesh_terms":[],"keywords":["Support vector machine","Margin (machine learning)","Regression","Quadratic programming","Computer science","Regression analysis","Polynomial regression","Artificial intelligence","Pattern recognition (psychology)","Machine learning","Mathematical optimization","Mathematics","Statistics"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Decent work and economic growth"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-05T15:28:09.288049Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}