{"id":"https://openalex.org/W1867341135","doi":"https://doi.org/10.1109/nnsp.2002.1030029","title":"Scaling of a length scale for regression and prediction","display_name":"Scaling of a length scale for regression and prediction","publication_year":2003,"publication_date":"2003-06-25","ids":{"openalex":"https://openalex.org/W1867341135","doi":"https://doi.org/10.1109/nnsp.2002.1030029","mag":"1867341135"},"language":"en","primary_location":{"id":"doi:10.1109/nnsp.2002.1030029","is_oa":false,"landing_page_url":"https://doi.org/10.1109/nnsp.2002.1030029","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5012702374","display_name":"Toshiaki Aida","orcid":null},"institutions":[{"id":"https://openalex.org/I163770644","display_name":"Okayama University","ror":"https://ror.org/02pc6pc55","country_code":"JP","type":"education","lineage":["https://openalex.org/I163770644"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"T. Aida","raw_affiliation_strings":["Department of Information Engineering, Okayama University, Okayama, Japan","Dept. of Inf. Eng., Okayama Univ., Japan"],"affiliations":[{"raw_affiliation_string":"Department of Information Engineering, Okayama University, Okayama, Japan","institution_ids":["https://openalex.org/I163770644"]},{"raw_affiliation_string":"Dept. of Inf. Eng., Okayama Univ., Japan","institution_ids":["https://openalex.org/I163770644"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5012702374"],"corresponding_institution_ids":["https://openalex.org/I163770644"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.08369459,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"179","last_page":"187"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13126","display_name":"Scientific Research and Discoveries","score":0.983299970626831,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13126","display_name":"Scientific Research and Discoveries","score":0.983299970626831,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9819999933242798,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10770","display_name":"Soil Geostatistics and Mapping","score":0.9724000096321106,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.6605626940727234},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.6559494733810425},{"id":"https://openalex.org/keywords/scaling","display_name":"Scaling","score":0.6152352690696716},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.6028938889503479},{"id":"https://openalex.org/keywords/variance","display_name":"Variance (accounting)","score":0.5755208730697632},{"id":"https://openalex.org/keywords/variation","display_name":"Variation (astronomy)","score":0.5377450585365295},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.5326293110847473},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5274629592895508},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4747600853443146},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.459138423204422},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.4435950219631195},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.42186370491981506},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3694979250431061},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3497576117515564}],"concepts":[{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.6605626940727234},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.6559494733810425},{"id":"https://openalex.org/C99844830","wikidata":"https://www.wikidata.org/wiki/Q102441924","display_name":"Scaling","level":2,"score":0.6152352690696716},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.6028938889503479},{"id":"https://openalex.org/C196083921","wikidata":"https://www.wikidata.org/wiki/Q7915758","display_name":"Variance (accounting)","level":2,"score":0.5755208730697632},{"id":"https://openalex.org/C2778334786","wikidata":"https://www.wikidata.org/wiki/Q1586270","display_name":"Variation (astronomy)","level":2,"score":0.5377450585365295},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.5326293110847473},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5274629592895508},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4747600853443146},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.459138423204422},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.4435950219631195},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.42186370491981506},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3694979250431061},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3497576117515564},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C44870925","wikidata":"https://www.wikidata.org/wiki/Q37547","display_name":"Astrophysics","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C121955636","wikidata":"https://www.wikidata.org/wiki/Q4116214","display_name":"Accounting","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/nnsp.2002.1030029","is_oa":false,"landing_page_url":"https://doi.org/10.1109/nnsp.2002.1030029","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 12th IEEE Workshop on Neural Networks for Signal Processing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W2004371148","https://openalex.org/W2010713800","https://openalex.org/W2032623929","https://openalex.org/W2055897374","https://openalex.org/W2058332274","https://openalex.org/W2962776596","https://openalex.org/W6675354405"],"related_works":["https://openalex.org/W2386430105","https://openalex.org/W2356521405","https://openalex.org/W2384358604","https://openalex.org/W1567829292","https://openalex.org/W2038534795","https://openalex.org/W3001063351","https://openalex.org/W31220157","https://openalex.org/W2288557197","https://openalex.org/W4233024177","https://openalex.org/W2101914902"],"abstract_inverted_index":{"We":[0],"analyze":[1],"the":[2,13,16,28,34,39,42,69,72],"prediction":[3],"from":[4],"noised":[5],"data,":[6,29],"based":[7],"on":[8],"a":[9,20,23,66,80],"regression":[10],"formulation":[11],"of":[12,36,41,44,71,74],"problem.":[14],"For":[15],"regression,":[17],"we":[18],"construct":[19],"model":[21,48],"with":[22],"length":[24],"scale":[25],"to":[26,51],"smooth":[27],"which":[30,78],"is":[31,49,58,79],"determined":[32],"by":[33],"variance":[35],"noise":[37],"and":[38],"speed":[40,70],"variation":[43,73],"original":[45,75],"signals.":[46],"The":[47],"found":[50],"be":[52],"effective":[53],"also":[54],"for":[55,83],"prediction.":[56,85],"This":[57],"because":[59],"it":[60],"decreases":[61],"an":[62],"uncertain":[63],"region":[64],"near":[65],"boundary":[67],"as":[68],"signals":[76],"increases,":[77],"crucial":[81],"property":[82],"accurate":[84]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}