{"doi":"10.1109/grc.2005.1547374","title":"Classification using support vector machines with graded resolution","abstract":null,"journal":"2005 IEEE International Conference on Granular Computing","year":2005,"id":46922,"datarank":2.257173327151013,"base_score":3.4657359027997265,"endowment":3.4657359027997265,"self_citation_contribution":0.519860385419959,"citation_network_contribution":1.737312941731054,"self_endowment_contribution":0.519860385419959,"citer_contribution":1.737312941731054,"corpus_percentile":null,"corpus_rank":null,"citation_count":31,"citer_count":31,"citers_with_citation_signal":27,"citers_with_endowment":27,"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":103944,"name":"Bing Liu","orcid":"0000-0002-6624-1128","position":1,"is_corresponding":false},{"id":216948,"name":"Chunru Wan","orcid":null,"position":2,"is_corresponding":false},{"id":216947,"name":"Lipo Wang","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Classification using support vector machines with graded resolution","abstract":"A method which we call support vector machine with graded resolution (SVM-GR) is proposed in this paper. During the training of the SVM-GR, we first form data granules to train the SVM-GR and remove those data granules that are not support vectors. We then use the remaining training samples to train the SVM-GR. Compared with the traditional SVM, our SVM-GR algorithm requires fewer training samples and support vectors, hence the computational time and memory requirements for the SVM-GR are much smaller than those of a conventional SVM that use the entire dataset. Experiments on benchmark data sets show that the generalization performance of the SVM-GR is comparable to the traditional SVM.","is_dataset_classified":null,"base_score":3.4657359027997265,"endowment":3.4657359027997265,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19910364","pmcid":null,"openalex_id":"https://openalex.org/W2090763504","authors":[],"funders":[],"total_grants":0,"fwci":0.8831,"citation_percentile":0.76408544,"influential_citations":1,"citation_trend":[{"year":2012,"count":1},{"year":2013,"count":6},{"year":2014,"count":7},{"year":2016,"count":2},{"year":2018,"count":3},{"year":2019,"count":1},{"year":2021,"count":1},{"year":2022,"count":1},{"year":2024,"count":1}],"oa_status":"closed","license":null,"oa_locations":[{"url":"http://xplorestaging.ieee.org/ielx5/10381/33011/01547374.pdf?arnumber=1547374","host_type":"publisher"},{"url":"https://doi.org/10.1109/grc.2005.1547374","host_type":""}],"fields_of_study":["Face and Expression Recognition","Text and Document Classification Technologies","Image Retrieval and Classification Techniques","Computer Science"],"mesh_terms":[],"keywords":["Support vector machine","Ranking SVM","Artificial intelligence","Generalization","Benchmark (surveying)","Computer science","Pattern recognition (psychology)","Structured support vector machine","Sequential minimal optimization","Machine learning","Mathematics"],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-14T23:40:54.027136Z","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":[]}