{"doi":"10.1109/apeie.2016.7806473","title":"New explanation of boosting efficiency in classification problem","abstract":null,"journal":"2016 13th International Scientific-Technical Conference on Actual Problems of Electronics Instrument Engineering (APEIE)","year":2016,"id":640281,"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":1663981,"name":"Victor M. Nedel'ko","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"New explanation of boosting efficiency in classification problem","abstract":"We study the reasons of high efficiency of methods based on classifiers compositions, such as boosting. It has been shown that one of the main grounds for such efficiency is the usage of the effect of independence of features. To investigate the performance of a method we run it directly on the distributions. We compare approximating capabilities of boosting and splines. Also we show the relation between a complexity and a margin.","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/W2571255492","authors":[],"funders":[],"total_grants":0,"fwci":0.0,"citation_percentile":0.23554789,"influential_citations":0,"citation_trend":[],"oa_status":"closed","license":null,"oa_locations":[{"url":"http://xplorestaging.ieee.org/ielx7/7795275/7806356/07806473.pdf?arnumber=7806473","host_type":"publisher"},{"url":"https://doi.org/10.1109/apeie.2016.7806473","host_type":"conference"}],"fields_of_study":["Advanced Statistical Methods and Models","Statistical and Computational Modeling","Advanced Computational Techniques in Science and Engineering"],"mesh_terms":[],"keywords":["Boosting (machine learning)","Computer science","Artificial intelligence","Machine learning","Margin (machine learning)","Independence (probability theory)","Pattern recognition (psychology)","Mathematics","Statistics"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Affordable and clean energy"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-07T09:56:06.619874Z","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":[]}