{"id":"https://openalex.org/W1843769552","doi":"https://doi.org/10.1109/igarss.2005.1525724","title":"Effect of prior probabilities on maximum likelihood classifier","display_name":"Effect of prior probabilities on maximum likelihood classifier","publication_year":2005,"publication_date":"2005-11-15","ids":{"openalex":"https://openalex.org/W1843769552","doi":"https://doi.org/10.1109/igarss.2005.1525724","mag":"1843769552"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2005.1525724","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2005.1525724","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05.","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":null,"display_name":"Zheng Mingguo","orcid":null},"institutions":[{"id":"https://openalex.org/I4210160793","display_name":"Institute of Geographic Sciences and Natural Resources Research","ror":"https://ror.org/04t1cdb72","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210160793"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zheng Mingguo","raw_affiliation_strings":["Institute of Geographical Sciences & Natural Resources Research, CAS, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute of Geographical Sciences & Natural Resources Research, CAS, Beijing, China","institution_ids":["https://openalex.org/I4210160793"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048878363","display_name":"Cai Qiangguo","orcid":null},"institutions":[{"id":"https://openalex.org/I4210160793","display_name":"Institute of Geographic Sciences and Natural Resources Research","ror":"https://ror.org/04t1cdb72","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210160793"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cai Qiangguo","raw_affiliation_strings":["Institute of Geographical Sciences & Natural Resources Research, CAS, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute of Geographical Sciences & Natural Resources Research, CAS, Beijing, China","institution_ids":["https://openalex.org/I4210160793"]}]},{"author_position":"last","author":{"id":null,"display_name":"Wang Zhenjian","orcid":null},"institutions":[{"id":"https://openalex.org/I196934937","display_name":"Liaocheng University","ror":"https://ror.org/03yh0n709","country_code":"CN","type":"education","lineage":["https://openalex.org/I196934937"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wang Zhenjian","raw_affiliation_strings":["Environment and Planning School, Liaocheng University, Liaocheng, China"],"affiliations":[{"raw_affiliation_string":"Environment and Planning School, Liaocheng University, Liaocheng, China","institution_ids":["https://openalex.org/I196934937"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210160793"],"apc_list":null,"apc_paid":null,"fwci":0.4986,"has_fulltext":false,"cited_by_count":12,"citation_normalized_percentile":{"value":0.67910385,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"6","issue":null,"first_page":"3753","last_page":"3756"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13890","display_name":"Remote Sensing and Land Use","score":0.9896000027656555,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13890","display_name":"Remote Sensing and Land Use","score":0.9896000027656555,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13734","display_name":"Advanced Computational Techniques and Applications","score":0.9470999836921692,"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.9423999786376953,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.7417160272598267},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.6762798428535461},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5667698979377747},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.550922155380249},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.546818733215332},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42993980646133423},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.40336257219314575},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3300986886024475}],"concepts":[{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.7417160272598267},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.6762798428535461},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5667698979377747},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.550922155380249},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.546818733215332},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42993980646133423},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.40336257219314575},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3300986886024475}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2005.1525724","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2005.1525724","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05.","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6499999761581421,"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1971419840","https://openalex.org/W2029820141","https://openalex.org/W2042809130","https://openalex.org/W2084502283","https://openalex.org/W2114475797","https://openalex.org/W2138973222","https://openalex.org/W2155120771","https://openalex.org/W2161772257","https://openalex.org/W2336802831","https://openalex.org/W6677053476"],"related_works":["https://openalex.org/W1922851888","https://openalex.org/W2961085424","https://openalex.org/W2406961220","https://openalex.org/W4224009465","https://openalex.org/W4306674287","https://openalex.org/W4286629047","https://openalex.org/W4205958290","https://openalex.org/W4384212932","https://openalex.org/W2046260256","https://openalex.org/W2049864679"],"abstract_inverted_index":{"Maximum":[0,19],"likelihood":[1,20,60],"classifier":[2,21,61],"is":[3,26,62,79],"widely":[4],"used":[5],"in":[6,43,85,106],"remote":[7],"sensing.":[8],"Many":[9],"researches":[10],"have":[11,34],"indicated":[12],"that":[13,29],"using":[14,93],"prior":[15,39,56,87,104],"probabilities":[16,40,88,105],"call":[17],"make":[18],"perform":[22],"better,":[23],"but":[24],"it":[25],"not":[27],"true":[28],"all":[30],"individual":[31],"classes":[32],"will":[33],"higher":[35],"classification":[36,73,102],"accuracy":[37],"after":[38],"are":[41,89],"incorporated":[42],"MLC.":[44],"In":[45],"the":[46,49,53,66,69,94,100],"present":[47],"paper,":[48],"theoretical":[50],"analysis":[51],"about":[52],"effect":[54,67],"of":[55],"probability":[57],"on":[58,68],"maximum":[59],"given":[63],"first,":[64],"including":[65],"discrimination":[70],"process":[71],"and":[72],"result.":[74],"Then":[75],"a":[76],"case":[77],"study":[78],"carried":[80],"out":[81],"by":[82,92],"one":[83],"approach":[84],"which":[86],"iteratively":[90],"modified":[91],"relative":[95],"area":[96],"proportion":[97],"derived":[98],"from":[99],"last":[101],"as":[103],"next":[107],"classification.":[108]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":1}],"updated_date":"2026-04-16T08:26:57.006410","created_date":"2025-10-10T00:00:00"}