{"id":"https://openalex.org/W2117276336","doi":"https://doi.org/10.1109/ijcnn.2004.1380905","title":"Fisher kernel for tree structured data","display_name":"Fisher kernel for tree structured data","publication_year":2005,"publication_date":"2005-01-31","ids":{"openalex":"https://openalex.org/W2117276336","doi":"https://doi.org/10.1109/ijcnn.2004.1380905","mag":"2117276336"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2004.1380905","doi":"https://doi.org/10.1109/ijcnn.2004.1380905","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2004.1380905","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"International Joint Conference on Neural Networks (IJCNN)","issn_l":"2161-4407","issn":["2161-4407"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-1","norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5060538474","display_name":"Luca Nicotra","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I108290504","display_name":"University of Pisa","ror":"https://ror.org/03ad39j10","country_code":"IT","type":"education","lineage":["https://openalex.org/I108290504"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"L. Nicotra","raw_affiliation_strings":["Department of Computer Science, University of Pisa, Pisa, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Pisa, Pisa, Italy","institution_ids":["https://openalex.org/I108290504"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010923663","display_name":"Alessio Micheli","orcid":"https://orcid.org/0000-0001-5764-5238","observed_orcids":["https://orcid.org/0000-0001-5764-5238"]},"institutions":[{"id":"https://openalex.org/I108290504","display_name":"University of Pisa","ror":"https://ror.org/03ad39j10","country_code":"IT","type":"education","lineage":["https://openalex.org/I108290504"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"A. Micheli","raw_affiliation_strings":["Department of Computer Science, University of Pisa, Pisa, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Pisa, Pisa, Italy","institution_ids":["https://openalex.org/I108290504"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019191173","display_name":"Antonina Starita","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I108290504","display_name":"University of Pisa","ror":"https://ror.org/03ad39j10","country_code":"IT","type":"education","lineage":["https://openalex.org/I108290504"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"A. Starita","raw_affiliation_strings":["Department of Computer Science, University of Pisa, Pisa, Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Science, University of Pisa, Pisa, Italy","institution_ids":["https://openalex.org/I108290504"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I108290504"],"apc_list":null,"apc_paid":null,"fwci":1.5009,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.81256926,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":87,"max":93},"biblio":{"volume":"3","issue":null,"first_page":"1917","last_page":"1922"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.3337000012397766,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.3337000012397766,"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/T10211","display_name":"Computational Drug Discovery Methods","score":0.14319999516010284,"subfield":{"id":"https://openalex.org/subfields/3002","display_name":"Drug Discovery"},"field":{"id":"https://openalex.org/fields/30","display_name":"Pharmacology, Toxicology and Pharmaceutics"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T12254","display_name":"Machine Learning in Bioinformatics","score":0.05700000002980232,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/fisher-kernel","display_name":"Fisher kernel","score":0.9980000257492065},{"id":"https://openalex.org/keywords/tree-structured-data","display_name":"tree-structured data","score":0.7710000276565552},{"id":"https://openalex.org/keywords/bayesian-networks","display_name":"Bayesian networks","score":0.8159999847412109},{"id":"https://openalex.org/keywords/quantitative-structure-property-relationship","display_name":"QSPR","score":0.7480000257492065}],"concepts":[{"id":"https://openalex.org/C207798031","wikidata":"https://www.wikidata.org/wiki/Q8563425","display_name":"Fisher kernel","level":5,"score":0.8096923828125},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6376365423202515},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5627015233039856},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5585146546363831},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.489505410194397},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.47342804074287415},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43835875391960144},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42598599195480347},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.41730836033821106},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4001540541648865},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3411858379840851},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.1706143021583557},{"id":"https://openalex.org/C181367576","wikidata":"https://www.wikidata.org/wiki/Q6394184","display_name":"Kernel Fisher discriminant analysis","level":4,"score":0.15988996624946594},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.11008140444755554},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0735088586807251}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/ijcnn.2004.1380905","doi":"https://doi.org/10.1109/ijcnn.2004.1380905","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2004.1380905","pdf_url":null,"source":{"id":"https://openalex.org/S4363607707","display_name":"International Joint Conference on Neural Networks (IJCNN)","issn_l":"2161-4407","issn":["2161-4407"],"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":["jufo-1","norway-1"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541)","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.109.7753","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.109.7753","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.math.unipd.it/~sperduti/PAPERI/nicotra.pdf","raw_type":"text"},{"id":"pmh:oai:arpi.unipi.it:11568/192313","doi":null,"is_oa":false,"landing_page_url":"http://hdl.handle.net/11568/192313","pdf_url":null,"source":{"id":"https://openalex.org/S4377196265","display_name":"CINECA IRIS Institutial research information system (University of Pisa)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I108290504","host_organization_name":"University of Pisa","host_organization_lineage":["https://openalex.org/I108290504"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1486089539","https://openalex.org/W1549899114","https://openalex.org/W1572659166","https://openalex.org/W1958945228","https://openalex.org/W1964357740","https://openalex.org/W2095684453","https://openalex.org/W2109722166","https://openalex.org/W2119755031","https://openalex.org/W2124225821","https://openalex.org/W2125838338","https://openalex.org/W2127713198","https://openalex.org/W2127827747","https://openalex.org/W2155682761","https://openalex.org/W2166473218","https://openalex.org/W2166681504","https://openalex.org/W2283504545","https://openalex.org/W4252136692","https://openalex.org/W4253573210","https://openalex.org/W4285719527"],"related_works":["https://openalex.org/W2113820595","https://openalex.org/W2784283104","https://openalex.org/W2743306992","https://openalex.org/W2476880392","https://openalex.org/W2112543852","https://openalex.org/W177184423","https://openalex.org/W1766743603","https://openalex.org/W2101841179","https://openalex.org/W2999075544","https://openalex.org/W2613525350"],"abstract_inverted_index":{"We":[0],"introduce":[1],"a":[2,28,32,48,62],"kernel":[3,14],"for":[4,16],"structured":[5],"data,":[6],"which":[7,37],"is":[8],"an":[9],"extension":[10],"of":[11,65],"the":[12,23,43,66],"Fisher":[13,24],"used":[15],"sequences.":[17],"In":[18],"our":[19],"approach,":[20],"we":[21],"extract":[22],"score":[25],"vectors":[26],"from":[27,42],"Bayesian":[29],"network,":[30],"specifically":[31],"hidden":[33],"tree":[34],"Markov":[35],"model,":[36],"can":[38],"be":[39],"constructed":[40],"starting":[41],"training":[44],"data.":[45],"Experiments":[46],"on":[47],"QSPR":[49],"(quantitative":[50],"structure-property":[51],"relationship)":[52],"analysis,":[53],"where":[54],"instances":[55],"are":[56],"naturally":[57],"represented":[58],"as":[59],"trees,":[60],"allow":[61],"first":[63],"test":[64],"approach.":[67]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2016-06-24T00:00:00"}
