{"doi":"10.1166/jmihi.2020.3085","title":"Multi-Source Transfer Learning Based on Inductive Knowledge-Leveraged for Medical Datasets","abstract":"<jats:p>Transfer learning changes the limitation of the same probability distribution among domains. There are many innovative ideas of those models which are fully used the information and the knowledge from different domains. Additional knowledge by transferring learning is beneficial to\n improve the learning ability in target tasks. However, most multiple source domain transfer learning algorithms are developed for the specified model. The existing transfer TGHRR algorithm is suitable to one source only. Given this problem, a new multiple source transfer learning algorithm\n integrated with the TGHRR and the inductive knowledge of multiple domains (MS-TGHRR in brevity) is proposed. Furthermore, MS-TGHRR algorithm has been evaluated by experiments on medical datasets for classification task. Extensive experiments demonstrate the classification accuracies trained\n by the newly designed MS-TGHRR algorithm over the existing multiple source transfer learning algorithms.</jats:p>","journal":"Journal of Medical Imaging and Health Informatics","year":2020,"id":606740,"datarank":0.10397207708399181,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"self_citation_contribution":0.10397207708399181,"citation_network_contribution":0.0,"self_endowment_contribution":0.10397207708399181,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1,"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":1318193,"name":"Weijie Wu","orcid":"0000-0002-4894-5981","position":1,"is_corresponding":false},{"id":1557711,"name":"Yanqing Shao","orcid":null,"position":2,"is_corresponding":false},{"id":1036882,"name":"Jingxiang Zhang","orcid":"0000-0002-5845-4887","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Multi-Source Transfer Learning Based on Inductive Knowledge-Leveraged for Medical Datasets","abstract":"<jats:p>Transfer learning changes the limitation of the same probability distribution among domains. There are many innovative ideas of those models which are fully used the information and the knowledge from different domains. Additional knowledge by transferring learning is beneficial to\n improve the learning ability in target tasks. However, most multiple source domain transfer learning algorithms are developed for the specified model. The existing transfer TGHRR algorithm is suitable to one source only. Given this problem, a new multiple source transfer learning algorithm\n integrated with the TGHRR and the inductive knowledge of multiple domains (MS-TGHRR in brevity) is proposed. Furthermore, MS-TGHRR algorithm has been evaluated by experiments on medical datasets for classification task. Extensive experiments demonstrate the classification accuracies trained\n by the newly designed MS-TGHRR algorithm over the existing multiple source transfer learning algorithms.</jats:p>","is_dataset_classified":null,"base_score":0.6931471805599453,"endowment":0.6931471805599453,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"23304386","pmcid":null,"openalex_id":"https://openalex.org/W3016888008","authors":[],"funders":[],"total_grants":0,"fwci":0.1304,"citation_percentile":0.52257799,"influential_citations":0,"citation_trend":[{"year":2023,"count":1}],"oa_status":"closed","license":null,"oa_locations":[{"url":"https://www.ingentaconnect.com/content/asp/jmihi/2020/00000010/00000007/art00023","host_type":"publisher"},{"url":"https://doi.org/10.1166/jmihi.2020.3085","host_type":"journal"}],"fields_of_study":["Domain Adaptation and Few-Shot Learning"],"mesh_terms":[],"keywords":["Inductive transfer","Transfer of learning","Computer science","Multi-task learning","Machine learning","Artificial intelligence","Task (project management)","Domain (mathematical analysis)","Knowledge transfer","Transfer (computing)","Robot learning","Mathematics","Knowledge management"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Quality Education"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-30T05:06:27.822576Z","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":[]}