{"id":"https://openalex.org/W2102851236","doi":"https://doi.org/10.5555/1756006.1756022","title":"Approximate Tree Kernels","display_name":"Approximate Tree Kernels","publication_year":2010,"publication_date":"2010-03-01","ids":{"openalex":"https://openalex.org/W2102851236","doi":"https://doi.org/10.5555/1756006.1756022","mag":"2102851236"},"language":"en","primary_location":{"id":"pmh:oai:CiteSeerX.psu:10.1.1.178.1209","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.178.1209","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://eprints.pascal-network.org/archive/00006892/01/2010-jmlr.pdf","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/A5066077721","display_name":"Konrad Rieck","orcid":"https://orcid.org/0000-0002-5054-8758","observed_orcids":["https://orcid.org/0000-0002-5054-8758"]},"institutions":[{"id":"https://openalex.org/I4577782","display_name":"Technische Universit\u00e4t Berlin","ror":"https://ror.org/03v4gjf40","country_code":"DE","type":"education","lineage":["https://openalex.org/I4577782"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"RieckKonrad","raw_affiliation_strings":["Technical University of Berlin,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technical University of Berlin,","institution_ids":["https://openalex.org/I4577782"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070612055","display_name":"Tammo Krueger","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I2800804238","display_name":"Fraunhofer Institute for Open Communication Systems","ror":"https://ror.org/00px80p03","country_code":"DE","type":"facility","lineage":["https://openalex.org/I2800804238","https://openalex.org/I4923324"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"KruegerTammo","raw_affiliation_strings":["Fraunhofer Inst. for Open Commun. Syst"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fraunhofer Inst. for Open Commun. Syst","institution_ids":["https://openalex.org/I2800804238"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049583553","display_name":"Ulf Brefeld","orcid":"https://orcid.org/0000-0001-9600-6463","observed_orcids":["https://orcid.org/0000-0001-9600-6463"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"BrefeldUlf","raw_affiliation_strings":["Professur f\u00fcr Wirtschaftsinformatik, insbesondere Machine Learning"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Professur f\u00fcr Wirtschaftsinformatik, insbesondere Machine Learning","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5107838719","display_name":"Klaus\u2010Robert M\u00fcller","orcid":"https://orcid.org/0000-0002-3861-7685","observed_orcids":["https://orcid.org/0000-0002-3861-7685"]},"institutions":[{"id":"https://openalex.org/I4577782","display_name":"Technische Universit\u00e4t Berlin","ror":"https://ror.org/03v4gjf40","country_code":"DE","type":"education","lineage":["https://openalex.org/I4577782"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"M\u00fcllerKlaus-Robert","raw_affiliation_strings":["Technical University of Berlin,"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Technical University of Berlin,","institution_ids":["https://openalex.org/I4577782"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.9363,"has_fulltext":false,"cited_by_count":22,"citation_normalized_percentile":{"value":0.90307321,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":88,"max":98},"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/T11550","display_name":"Text and Document Classification Technologies","score":0.31470000743865967,"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/T11550","display_name":"Text and Document Classification Technologies","score":0.31470000743865967,"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/T10538","display_name":"Data Mining Algorithms and Applications","score":0.19449999928474426,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/T12072","display_name":"Machine Learning and Algorithms","score":0.10440000146627426,"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/tree-kernels","display_name":"tree kernels","score":0.9039999842643738},{"id":"https://openalex.org/keywords/parse-trees","display_name":"parse trees","score":0.699999988079071},{"id":"https://openalex.org/keywords/kernel-approximation","display_name":"kernel approximation","score":0.6919999718666077},{"id":"https://openalex.org/keywords/linear-programming","display_name":"linear programming","score":0.6420000195503235},{"id":"https://openalex.org/keywords/tree-structured-data","display_name":"tree-structured data","score":0.7049999833106995},{"id":"https://openalex.org/keywords/html-documents","display_name":"HTML documents","score":0.6480000019073486},{"id":"https://openalex.org/keywords/kernel-learning","display_name":"kernel learning","score":0.6330000162124634},{"id":"https://openalex.org/keywords/computational-efficiency","display_name":"computational efficiency","score":0.5879999995231628},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"unsupervised learning","score":0.609000027179718},{"id":"https://openalex.org/keywords/feature-space-dimensionality","display_name":"feature space dimensionality","score":0.6470000147819519}],"concepts":[{"id":"https://openalex.org/C140417398","wikidata":"https://www.wikidata.org/wiki/Q16933942","display_name":"Tree kernel","level":5,"score":0.7360707521438599},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.6975598335266113},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6392598152160645},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5775372385978699},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5319129824638367},{"id":"https://openalex.org/C163797641","wikidata":"https://www.wikidata.org/wiki/Q2067937","display_name":"Tree structure","level":3,"score":0.4697663486003876},{"id":"https://openalex.org/C84927040","wikidata":"https://www.wikidata.org/wiki/Q6057306","display_name":"Interval tree","level":4,"score":0.4680319130420685},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44385018944740295},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.4427618980407715},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.41954460740089417},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39258790016174316},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3894651234149933},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36863958835601807},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.33684682846069336},{"id":"https://openalex.org/C197855036","wikidata":"https://www.wikidata.org/wiki/Q380172","display_name":"Binary tree","level":2,"score":0.279712438583374},{"id":"https://openalex.org/C195699287","wikidata":"https://www.wikidata.org/wiki/Q7915722","display_name":"Variable kernel density estimation","level":4,"score":0.22879046201705933},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.1070760190486908},{"id":"https://openalex.org/C118615104","wikidata":"https://www.wikidata.org/wiki/Q121416","display_name":"Discrete mathematics","level":1,"score":0.10383206605911255},{"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:10.1.1.178.1209","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.178.1209","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://eprints.pascal-network.org/archive/00006892/01/2010-jmlr.pdf","raw_type":"text"},{"id":"pmh:oai:fraunhofer.de:N-133844","doi":null,"is_oa":false,"landing_page_url":"http://publica.fraunhofer.de/documents/N-133844.html","pdf_url":null,"source":{"id":"https://openalex.org/S4306400801","display_name":"Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I4923324","host_organization_name":"Fraunhofer-Gesellschaft","host_organization_lineage":["https://openalex.org/I4923324"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Fraunhofer FIRST","raw_type":"Journal Article"},{"id":"pmh:oai:publica.fraunhofer.de:publica/221709","doi":null,"is_oa":false,"landing_page_url":"https://publica.fraunhofer.de/handle/publica/221709","pdf_url":null,"source":{"id":"https://openalex.org/S4306400318","display_name":"Fraunhofer-Publica (Fraunhofer-Gesellschaft)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I4923324","host_organization_name":"Fraunhofer-Gesellschaft","host_organization_lineage":["https://openalex.org/I4923324"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"journal article"},{"id":"mag:3005055492","doi":"https://doi.org/10.5555/1756006.1756022","is_oa":false,"landing_page_url":"https://dl.acm.org/doi/10.5555/1756006.1756022","pdf_url":null,"source":{"id":"https://openalex.org/S118988714","display_name":"Journal of Machine Learning Research","issn_l":"1532-4435","issn":["1532-4435","1533-7928"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","ccf-a","cwts-core","dongbi-a","jufo-3","ki-jl-2","norway-2","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310315718","host_organization_name":"The MIT Press","host_organization_lineage":["https://openalex.org/P4310315718","https://openalex.org/P4310316440"],"host_organization_lineage_names":["The MIT Press","Massachusetts Institute of Technology"],"type":"journal"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"Journal of Machine Learning Research","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W740415","https://openalex.org/W20965513","https://openalex.org/W100509257","https://openalex.org/W224064951","https://openalex.org/W1482382132","https://openalex.org/W1486401986","https://openalex.org/W1510073064","https://openalex.org/W1566480186","https://openalex.org/W1574901103","https://openalex.org/W1575978290","https://openalex.org/W1576213419","https://openalex.org/W1587871245","https://openalex.org/W1605667540","https://openalex.org/W1779735989","https://openalex.org/W1860995734","https://openalex.org/W1978468533","https://openalex.org/W1999427165","https://openalex.org/W2002089154","https://openalex.org/W2070246124","https://openalex.org/W2073562093","https://openalex.org/W2086004682","https://openalex.org/W2086437504","https://openalex.org/W2096623622","https://openalex.org/W2099848066","https://openalex.org/W2100294832","https://openalex.org/W2101053723","https://openalex.org/W2103378897","https://openalex.org/W2105732805","https://openalex.org/W2106560376","https://openalex.org/W2108995755","https://openalex.org/W2112400959","https://openalex.org/W2124225821","https://openalex.org/W2132870739","https://openalex.org/W2139148100","https://openalex.org/W2140095548","https://openalex.org/W2141950720","https://openalex.org/W2142387771","https://openalex.org/W2151787203","https://openalex.org/W2151928840","https://openalex.org/W2156909104","https://openalex.org/W2158413235","https://openalex.org/W2172163424","https://openalex.org/W2752853835","https://openalex.org/W2947657523","https://openalex.org/W3119651796","https://openalex.org/W3135760699"],"related_works":["https://openalex.org/W7100894876","https://openalex.org/W2101053723","https://openalex.org/W143678110","https://openalex.org/W7096514363","https://openalex.org/W2743736970","https://openalex.org/W1987504843","https://openalex.org/W7100093928","https://openalex.org/W7100884968","https://openalex.org/W2237235759","https://openalex.org/W7100003882"],"abstract_inverted_index":{"Convolution":[0],"kernels":[1,17,49,67,115,140],"for":[2,7,60,100,104],"trees":[3],"provide":[4],"simple":[5],"means":[6],"learning":[8,90,108],"with":[9],"tree-structured":[10],"data.":[11],"The":[12,64],"computation":[13,71],"time":[14],"of":[15,23,29,76,88,123,130],"tree":[16,62,66,114,132,139],"is":[18],"quadratic":[19],"in":[20,151],"the":[21,24,112,127,137],"size":[22],"trees,":[25,38],"since":[26],"all":[27],"pairs":[28],"nodes":[30],"need":[31],"to":[32,72,120,143],"be":[33],"compared.":[34],"Thus,":[35],"large":[36],"parse":[37,61],"obtained":[39],"from":[40],"HTML":[41],"documents":[42],"or":[43],"structured":[44],"network":[45],"data,":[46],"render":[47],"convolution":[48],"inapplicable.":[50],"In":[51],"this":[52],"article,":[53],"we":[54],"propose":[55],"an":[56],"effective":[57],"approximation":[58],"technique":[59],"kernels.":[63,133],"approximate":[65,113,138],"(ATKs)":[68],"limit":[69],"kernel":[70],"a":[73],"sparse":[74],"subset":[75],"relevant":[77,149],"subtrees":[78],"and":[79,86,106],"discard":[80],"redundant":[81],"structures,":[82],"such":[83,102],"that":[84],"training":[85],"testing":[87],"kernel-based":[89],"methods":[91],"are":[92],"significantly":[93],"accelerated.":[94],"We":[95],"devise":[96],"linear":[97],"programming":[98],"approaches":[99],"identifying":[101,148],"subsets":[103],"supervised":[105],"unsupervised":[107,135],"tasks,":[109,136],"respectively.":[110],"Empirically,":[111],"attain":[116],"run-time":[117],"improvements":[118],"up":[119],"three":[121],"orders":[122],"magnitude":[124],"while":[125],"preserving":[126],"predictive":[128],"accuracy":[129],"regular":[131],"For":[134],"even":[141],"lead":[142],"more":[144],"accurate":[145],"predictions":[146],"by":[147],"dimensions":[150],"feature":[152],"space.":[153]},"counts_by_year":[{"year":2021,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":3},{"year":2014,"cited_by_count":2},{"year":2013,"cited_by_count":1},{"year":2012,"cited_by_count":5}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2016-06-24T00:00:00"}
