{"id":"https://openalex.org/W2105425186","doi":"https://doi.org/10.1109/ijcnn.2011.6033371","title":"A nonparametric information theoretic approach for change detection in time series","display_name":"A nonparametric information theoretic approach for change detection in time series","publication_year":2011,"publication_date":"2011-07-01","ids":{"openalex":"https://openalex.org/W2105425186","doi":"https://doi.org/10.1109/ijcnn.2011.6033371","mag":"2105425186"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn.2011.6033371","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2011.6033371","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 2011 International Joint Conference on Neural Networks","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/A5043053530","display_name":"Songlin Zhao","orcid":null},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Songlin Zhao","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Florida, Gainesville, USA","Department of Electrical and Computer Engineering, The University of Florida, Gainesville, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Florida, Gainesville, USA","institution_ids":["https://openalex.org/I33213144"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, The University of Florida, Gainesville, USA","institution_ids":["https://openalex.org/I33213144"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019504861","display_name":"Jos\u00e9 C. Pr\u0131\u0301ncipe","orcid":"https://orcid.org/0000-0002-3449-3531"},"institutions":[{"id":"https://openalex.org/I33213144","display_name":"University of Florida","ror":"https://ror.org/02y3ad647","country_code":"US","type":"education","lineage":["https://openalex.org/I33213144"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jose C. Principe","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Florida, Gainesville, USA","Department of Electrical and Computer Engineering, The University of Florida, Gainesville, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Florida, Gainesville, USA","institution_ids":["https://openalex.org/I33213144"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering, The University of Florida, Gainesville, USA","institution_ids":["https://openalex.org/I33213144"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5043053530"],"corresponding_institution_ids":["https://openalex.org/I33213144"],"apc_list":null,"apc_paid":null,"fwci":0.4402,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.74001407,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"20","issue":null,"first_page":"1281","last_page":"1284"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9980999827384949,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9980999827384949,"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/T10711","display_name":"Target Tracking and Data Fusion in Sensor Networks","score":0.9896000027656555,"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/T12261","display_name":"Statistical Mechanics and Entropy","score":0.9807000160217285,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/surprise","display_name":"Surprise","score":0.7193408608436584},{"id":"https://openalex.org/keywords/nonparametric-statistics","display_name":"Nonparametric statistics","score":0.6515410542488098},{"id":"https://openalex.org/keywords/series","display_name":"Series (stratigraphy)","score":0.5861067175865173},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.545598030090332},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.520846426486969},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.5185490250587463},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4717569947242737},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.46031448245048523},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3869681656360626},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.3803693652153015},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.33772701025009155},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.32936686277389526},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.305285781621933},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.213512122631073}],"concepts":[{"id":"https://openalex.org/C2780343955","wikidata":"https://www.wikidata.org/wiki/Q333173","display_name":"Surprise","level":2,"score":0.7193408608436584},{"id":"https://openalex.org/C102366305","wikidata":"https://www.wikidata.org/wiki/Q1097688","display_name":"Nonparametric statistics","level":2,"score":0.6515410542488098},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.5861067175865173},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.545598030090332},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.520846426486969},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.5185490250587463},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4717569947242737},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.46031448245048523},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3869681656360626},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3803693652153015},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.33772701025009155},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32936686277389526},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.305285781621933},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.213512122631073},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0},{"id":"https://openalex.org/C77805123","wikidata":"https://www.wikidata.org/wiki/Q161272","display_name":"Social psychology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/ijcnn.2011.6033371","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn.2011.6033371","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The 2011 International Joint Conference on Neural Networks","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.642.9146","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.642.9146","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cnel.ufl.edu/files/1317347633.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W243812323","https://openalex.org/W1497683195","https://openalex.org/W1528401293","https://openalex.org/W1746819321","https://openalex.org/W1967993709","https://openalex.org/W2012915481","https://openalex.org/W2034699324","https://openalex.org/W2039546655","https://openalex.org/W2061974965","https://openalex.org/W2068066383","https://openalex.org/W2126709692","https://openalex.org/W2130315424","https://openalex.org/W2133671888","https://openalex.org/W2153290280","https://openalex.org/W2167932108","https://openalex.org/W2481926318","https://openalex.org/W2801840425","https://openalex.org/W3003694668","https://openalex.org/W4210509282","https://openalex.org/W4211049957","https://openalex.org/W4246607039"],"related_works":["https://openalex.org/W4236382845","https://openalex.org/W4388712630","https://openalex.org/W2481168998","https://openalex.org/W2476994687","https://openalex.org/W642988558","https://openalex.org/W2324507472","https://openalex.org/W1999899047","https://openalex.org/W2173353921","https://openalex.org/W2810824260","https://openalex.org/W4385750187"],"abstract_inverted_index":{"This":[0],"paper":[1],"presents":[2],"an":[3,24],"online":[4,46],"nonparametric":[5],"methodology":[6,75],"based":[7,22,98],"on":[8,23,52,76,99],"the":[9,17,30,57,74,95,100],"Kernel":[10],"Least":[11],"Mean":[12],"Square":[13],"(KLMS)":[14],"algorithm":[15,59],"and":[16,42],"surprise":[18,62,90],"criterion,":[19],"which":[20],"is":[21,92],"information":[25,33],"theoretic":[26],"framework.":[27],"Surprise":[28],"quantifies":[29],"amount":[31],"of":[32],"a":[34,38,77],"datum":[35],"contains":[36],"given":[37],"known":[39],"system":[40],"state,":[41],"can":[43],"be":[44],"estimated":[45],"using":[47],"Gaussian":[48],"Process":[49],"Theory.":[50],"Based":[51],"this":[53,84],"concept,":[54],"we":[55],"use":[56],"KLMS":[58],"together":[60],"with":[61],"criterion":[63,91],"to":[64,82],"detect":[65],"regime":[66],"change":[67],"in":[68],"nonstationary":[69],"time":[70,80],"series.":[71],"We":[72],"test":[73],"synthesized":[78],"chaotic":[79],"series":[81],"illustrate":[83],"criterion.":[85,102],"The":[86],"results":[87],"show":[88],"that":[89],"better":[93],"than":[94],"conventional":[96],"segmentation":[97],"error":[101]},"counts_by_year":[{"year":2014,"cited_by_count":1}],"updated_date":"2026-04-05T17:49:38.594831","created_date":"2025-10-10T00:00:00"}
