{"id":"https://openalex.org/W2765859928","doi":"https://doi.org/10.1007/978-3-319-68612-7_80","title":"Applying the Heavy-Tailed Kernel to\u00a0the\u00a0Gaussian Process Regression for\u00a0Modeling\u00a0Point\u00a0of Sale Data","display_name":"Applying the Heavy-Tailed Kernel to\u00a0the\u00a0Gaussian Process Regression for\u00a0Modeling\u00a0Point\u00a0of Sale Data","publication_year":2017,"publication_date":"2017-01-01","ids":{"openalex":"https://openalex.org/W2765859928","doi":"https://doi.org/10.1007/978-3-319-68612-7_80","mag":"2765859928"},"language":"en","primary_location":{"id":"doi:10.1007/978-3-319-68612-7_80","is_oa":false,"landing_page_url":"https://doi.org/10.1007/978-3-319-68612-7_80","pdf_url":null,"source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"},"type":"conference-paper","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/A5030499511","display_name":"Rui Yang","orcid":"https://orcid.org/0000-0002-5634-5476"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Rui Yang","raw_affiliation_strings":["Graduate School of Engineering, The University of Tokyo, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Engineering, The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5060604714","display_name":"Yukio Ohsawa","orcid":"https://orcid.org/0000-0003-2943-2547"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Yukio Ohsawa","raw_affiliation_strings":["Graduate School of Engineering, The University of Tokyo, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Engineering, The University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5030499511","https://openalex.org/A5060604714"],"corresponding_institution_ids":["https://openalex.org/I74801974"],"apc_list":{"value":5000,"currency":"EUR","value_usd":5392},"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.20181818,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"705","last_page":"712"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9218999743461609,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9218999743461609,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6624370217323303},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6503968238830566},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6072760820388794},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.5945147275924683},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5298033952713013},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.49531546235084534},{"id":"https://openalex.org/keywords/kernel-regression","display_name":"Kernel regression","score":0.4672679007053375},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.41359928250312805},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.391488641500473},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.34802016615867615},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3335530459880829},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.3052968978881836},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2544959485530853}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6624370217323303},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6503968238830566},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6072760820388794},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.5945147275924683},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5298033952713013},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49531546235084534},{"id":"https://openalex.org/C200695384","wikidata":"https://www.wikidata.org/wiki/Q1739319","display_name":"Kernel regression","level":3,"score":0.4672679007053375},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.41359928250312805},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.391488641500473},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34802016615867615},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3335530459880829},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.3052968978881836},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2544959485530853},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"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/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/978-3-319-68612-7_80","is_oa":false,"landing_page_url":"https://doi.org/10.1007/978-3-319-68612-7_80","pdf_url":null,"source":{"id":"https://openalex.org/S106296714","display_name":"Lecture notes in computer science","issn_l":"0302-9743","issn":["0302-9743","1611-3349"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"book series"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Lecture Notes in Computer Science","raw_type":"book-chapter"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Decent work and economic growth","score":0.5,"id":"https://metadata.un.org/sdg/8"}],"awards":[{"id":"https://openalex.org/G3225796455","display_name":"Analysis and visualization of sequential data for discovery without learning","funder_award_id":"16K12428","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":17,"referenced_works":["https://openalex.org/W76947095","https://openalex.org/W1560724230","https://openalex.org/W1563857041","https://openalex.org/W1875842236","https://openalex.org/W2030635930","https://openalex.org/W2117853077","https://openalex.org/W2168353136","https://openalex.org/W2170036396","https://openalex.org/W2307774491","https://openalex.org/W2427628320","https://openalex.org/W2498094064","https://openalex.org/W2963641874","https://openalex.org/W3099514962","https://openalex.org/W4211049957","https://openalex.org/W4292023222","https://openalex.org/W6767070838","https://openalex.org/W6813542777"],"related_works":["https://openalex.org/W1493568480","https://openalex.org/W2995727521","https://openalex.org/W4385967523","https://openalex.org/W2380708104","https://openalex.org/W2294970809","https://openalex.org/W2213164457","https://openalex.org/W2115519811","https://openalex.org/W2157272197","https://openalex.org/W2246292763","https://openalex.org/W2108670750"],"abstract_inverted_index":null,"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-08-04T08:18:43.703281","created_date":"2025-10-10T00:00:00"}
