{"id":"https://openalex.org/W133270979","doi":null,"title":"Function Approximation with Neural Networks and Local Methods: Bias, Variance and Smoothness","display_name":"Function Approximation with Neural Networks and Local Methods: Bias, Variance and Smoothness","publication_year":1996,"publication_date":"1996-01-01","ids":{"openalex":"https://openalex.org/W133270979","mag":"133270979"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.27.9636","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.27.9636","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.lce.hut.fi/~jlampine/papers/Lawrence-acnn96-a4.ps.gz","raw_type":"text"},"volume":null,"type":"article","is_traditional":true,"indexed_in":[],"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/A5112573274","display_name":"Steve Lawrence","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Steve Lawrence","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5077581403","display_name":"Ah Chung Tsoi","orcid":"https://orcid.org/0000-0003-2904-7008","observed_orcids":["https://orcid.org/0000-0003-2904-7008"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ah Chung Tsoi","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-2904-7008","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5086895345","display_name":"Andrew D. Back","orcid":"https://orcid.org/0000-0001-5474-1910","observed_orcids":["https://orcid.org/0000-0001-5474-1910"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Andrew D. Back","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-5474-1910","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.3686,"has_fulltext":false,"cited_by_count":77,"citation_normalized_percentile":{"value":0.87662557,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":97},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9878000020980835,"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"}},"topics":[{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.9878000020980835,"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/T12676","display_name":"Machine Learning and ELM","score":0.006399999838322401,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.0010000000474974513,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/function-approximation","display_name":"function approximation","score":0.9819999933242798},{"id":"https://openalex.org/keywords/neural-network","display_name":"artificial neural networks","score":0.824999988079071},{"id":"https://openalex.org/keywords/nearest-neighbors","display_name":"nearest neighbors","score":0.47699999809265137},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"multilayer perceptron","score":0.9559999704360962},{"id":"https://openalex.org/keywords/bias-variance-trade-off","display_name":"bias-variance trade-off","score":0.7860000133514404},{"id":"https://openalex.org/keywords/smoothness","display_name":"smoothness","score":0.7820000052452087},{"id":"https://openalex.org/keywords/building-energy-consumption-prediction","display_name":"building energy consumption prediction","score":0.6439999938011169},{"id":"https://openalex.org/keywords/mackey-glass-time-series","display_name":"Mackey-Glass time series","score":0.7609999775886536},{"id":"https://openalex.org/keywords/sunspot-time-series","display_name":"sunspot time series","score":0.7490000128746033}],"concepts":[{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5671342015266418},{"id":"https://openalex.org/C91873725","wikidata":"https://www.wikidata.org/wiki/Q3445816","display_name":"Function approximation","level":3,"score":0.5589258670806885},{"id":"https://openalex.org/C102634674","wikidata":"https://www.wikidata.org/wiki/Q868473","display_name":"Smoothness","level":2,"score":0.532873809337616},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4932958483695984},{"id":"https://openalex.org/C53533937","wikidata":"https://www.wikidata.org/wiki/Q185020","display_name":"Histogram","level":3,"score":0.47953009605407715},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.47876229882240295},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.4224797487258911},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.40026646852493286},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3985305726528168},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.39791715145111084},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3661370277404785},{"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"pmh:oai:CiteSeerX.psu:10.1.1.27.9636","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.27.9636","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.lce.hut.fi/~jlampine/papers/Lawrence-acnn96-a4.ps.gz","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.38.6373","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.38.6373","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.neci.nec.com/~lawrence/papers/local-acnn96/local-acnn96.ps.gz","raw_type":"text"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.51.2334","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.51.2334","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.neci.nj.nec.com/homepages/lawrence/papers/local-acnn96/local-acnn96-a4.ps.gz","raw_type":"text"},{"id":"mag:133270979","doi":null,"is_oa":false,"landing_page_url":"http://machine-learning.martinsewell.com/ann/LaTB96.pdf","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1662665","https://openalex.org/W1498759836","https://openalex.org/W1504694836","https://openalex.org/W1554511749","https://openalex.org/W1594031697","https://openalex.org/W1606326889","https://openalex.org/W1967320885","https://openalex.org/W1976990135","https://openalex.org/W2015332086","https://openalex.org/W2024668293","https://openalex.org/W2025653905","https://openalex.org/W2076118331","https://openalex.org/W2089367368","https://openalex.org/W2090581513","https://openalex.org/W2094631910","https://openalex.org/W2102201073","https://openalex.org/W2103496339","https://openalex.org/W2123507038","https://openalex.org/W2125055259","https://openalex.org/W2130695501","https://openalex.org/W2135346934","https://openalex.org/W2137958459","https://openalex.org/W2137983211","https://openalex.org/W2147169375","https://openalex.org/W2147647517","https://openalex.org/W2155677642","https://openalex.org/W2165558283","https://openalex.org/W2896916590","https://openalex.org/W3085162807"],"related_works":["https://openalex.org/W7097316013","https://openalex.org/W7064076843","https://openalex.org/W7079727982","https://openalex.org/W2539379954","https://openalex.org/W2123507038","https://openalex.org/W7095833084","https://openalex.org/W7095809604","https://openalex.org/W6336603","https://openalex.org/W7017321408","https://openalex.org/W2399641486"],"abstract_inverted_index":{"We":[0,88],"review":[1],"the":[2,22,29,40,48,59,64,69,85,91,98,107,112,122,125,141,146],"use":[3],"of":[4,21,106,111,140],"global":[5,44],"and":[6,33,58,84],"local":[7,54,93,126],"methods":[8,30],"for":[9,145],"estimating":[10],"a":[11,52],"function":[12,23],"mapping":[13],"R":[14,17],"m":[15],")":[16],"n":[18],"from":[19],"samples":[20],"containing":[24],"noise.":[25],"The":[26],"relationship":[27],"between":[28],"is":[31,37],"examined":[32],"an":[34],"empirical":[35],"comparison":[36],"performed":[38],"using":[39],"multi-layer":[41],"perceptron":[42],"(MLP)":[43],"neural":[45],"network":[46],"model,":[47,51,57],"single":[49],"nearest-neighbour":[50],"linear":[53],"approximation":[55,94,127],"(LA)":[56],"following":[60],"commonly":[61],"used":[62,118],"datasets:":[63],"Mackey-Glass":[65],"chaotic":[66],"time":[67,71],"series,":[68,72],"Sunspot":[70],"British":[73],"English":[74],"Vowel":[75],"data,":[76,83],"TIMIT":[77],"speech":[78],"phonemes,":[79],"building":[80],"energy":[81],"prediction":[82],"sonar":[86],"dataset.":[87],"find":[89,134],"that":[90,135],"simple":[92],"models":[95],"often":[96],"outperform":[97],"MLP.":[99],"No":[100],"criterion":[101],"such":[102],"as":[103],"classification/prediction,":[104],"size":[105],"training":[108,113,147],"set,":[109,114],"dimensionality":[110],"etc.":[115],"can":[116,151],"be":[117,130],"to":[119],"distinguish":[120],"whether":[121],"MLP":[123],"or":[124],"method":[128],"will":[129],"superior.":[131],"However,":[132],"we":[133,137,150],"if":[136],"consider":[138],"histograms":[139],"k-NN":[142],"density":[143],"estimates":[144],"datasets":[148],"then":[149],"choose":[152],"th...":[153]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":4},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":4},{"year":2012,"cited_by_count":3}],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2016-06-24T00:00:00"}
