Selecting most adaptable diagnostic solutions through Pivoting-Based Retrieval
The aim of the present paper is to investigate a retrieval strategy for case-based diagnosis called Pivoting Based Retrieval (PBR), based on a tight integration between retrieval and adaptation estimation. It exploits a heuristic estimate of the adaptability of a solution; during retrieval, lower an...
Gespeichert in:
| Hauptverfasser: | , , |
|---|---|
| Format: | Tagungsbericht |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 402 |
|---|---|
| container_issue | |
| container_start_page | 393 |
| container_title | |
| container_volume | |
| creator | Portinale, Luigi Torasso, Pietro Magro, Diego |
| description | The aim of the present paper is to investigate a retrieval strategy for case-based diagnosis called Pivoting Based Retrieval (PBR), based on a tight integration between retrieval and adaptation estimation. It exploits a heuristic estimate of the adaptability of a solution; during retrieval, lower and upper bounds for such an estimate are computed for relevant cases and a pivot case is selected, determining which cases have to be considered and which have not. Such a technique has been evaluated on three different domain models and very satisfactory results have been obtained both in terms of accuracy, space and retrieval time |
| doi_str_mv | 10.1007/3-540-63233-6_509 |
| format | Conference Proceeding |
| fullrecord | <record><control><sourceid>pascalfrancis_sprin</sourceid><recordid>TN_cdi_pascalfrancis_primary_2732217</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2732217</sourcerecordid><originalsourceid>FETCH-LOGICAL-j290t-626a3b186b86f28235e5086a3098560455a02740fecc1f63e5ccf4b435b3b2883</originalsourceid><addsrcrecordid>eNpFkMlOwzAQhs0mUZU-ALccuJqOPbbjHKFikypALGfLcZ3UJU2qOK3E2-MWJOYy0r-MNB8hlwyuGUA-RSoFUIUckSojoTgikyLXmFRVcNTqmIyYYowiiuLk39sX8lMyAgROi1zgOZnEuII0yFkuxYg8v_vGuyG0dbbu4pDZhd0Mtmx8tgi2bpMUXBa7ZjuEro3ZsOy7bb3MXsOu25forY1-kb35oQ9-Z5sLclbZJvrJ3x6Tz_u7j9kjnb88PM1u5nTFCxio4spiybQqtaq45ii9BJ00KLRUIKS0wHMBlXeOVQq9dK4SpUBZYsm1xjG5-r27sdHZpupt60I0mz6sbf9teI48PZhi099YTE5b-96UXfcVDQOzB2vQJEzmwMkcwOIPfNJlkQ</addsrcrecordid><sourcetype>Index Database</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Selecting most adaptable diagnostic solutions through Pivoting-Based Retrieval</title><source>Springer Books</source><creator>Portinale, Luigi ; Torasso, Pietro ; Magro, Diego</creator><contributor>Leake, David B. ; Plaza, Enric</contributor><creatorcontrib>Portinale, Luigi ; Torasso, Pietro ; Magro, Diego ; Leake, David B. ; Plaza, Enric</creatorcontrib><description>The aim of the present paper is to investigate a retrieval strategy for case-based diagnosis called Pivoting Based Retrieval (PBR), based on a tight integration between retrieval and adaptation estimation. It exploits a heuristic estimate of the adaptability of a solution; during retrieval, lower and upper bounds for such an estimate are computed for relevant cases and a pivot case is selected, determining which cases have to be considered and which have not. Such a technique has been evaluated on three different domain models and very satisfactory results have been obtained both in terms of accuracy, space and retrieval time</description><identifier>ISSN: 0302-9743</identifier><identifier>ISBN: 9783540632337</identifier><identifier>ISBN: 3540632336</identifier><identifier>EISSN: 1611-3349</identifier><identifier>EISBN: 9783540692386</identifier><identifier>EISBN: 354069238X</identifier><identifier>DOI: 10.1007/3-540-63233-6_509</identifier><language>eng</language><publisher>Berlin, Heidelberg: Springer Berlin Heidelberg</publisher><subject>Adaptation Effort ; Applied sciences ; Artificial intelligence ; Case Memory ; Computer science; control theory; systems ; Exact sciences and technology ; Heuristic Estimate ; Input Case ; Learning and adaptive systems ; Retrieval Time</subject><ispartof>Case-Based Reasoning Research and Development, 1997, p.393-402</ispartof><rights>Springer-Verlag Berlin Heidelberg 1997</rights><rights>1997 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/3-540-63233-6_509$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/3-540-63233-6_509$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>309,310,775,776,780,785,786,789,27902,38232,41418,42487</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=2732217$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><contributor>Leake, David B.</contributor><contributor>Plaza, Enric</contributor><creatorcontrib>Portinale, Luigi</creatorcontrib><creatorcontrib>Torasso, Pietro</creatorcontrib><creatorcontrib>Magro, Diego</creatorcontrib><title>Selecting most adaptable diagnostic solutions through Pivoting-Based Retrieval</title><title>Case-Based Reasoning Research and Development</title><description>The aim of the present paper is to investigate a retrieval strategy for case-based diagnosis called Pivoting Based Retrieval (PBR), based on a tight integration between retrieval and adaptation estimation. It exploits a heuristic estimate of the adaptability of a solution; during retrieval, lower and upper bounds for such an estimate are computed for relevant cases and a pivot case is selected, determining which cases have to be considered and which have not. Such a technique has been evaluated on three different domain models and very satisfactory results have been obtained both in terms of accuracy, space and retrieval time</description><subject>Adaptation Effort</subject><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Case Memory</subject><subject>Computer science; control theory; systems</subject><subject>Exact sciences and technology</subject><subject>Heuristic Estimate</subject><subject>Input Case</subject><subject>Learning and adaptive systems</subject><subject>Retrieval Time</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>9783540632337</isbn><isbn>3540632336</isbn><isbn>9783540692386</isbn><isbn>354069238X</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1997</creationdate><recordtype>conference_proceeding</recordtype><recordid>eNpFkMlOwzAQhs0mUZU-ALccuJqOPbbjHKFikypALGfLcZ3UJU2qOK3E2-MWJOYy0r-MNB8hlwyuGUA-RSoFUIUckSojoTgikyLXmFRVcNTqmIyYYowiiuLk39sX8lMyAgROi1zgOZnEuII0yFkuxYg8v_vGuyG0dbbu4pDZhd0Mtmx8tgi2bpMUXBa7ZjuEro3ZsOy7bb3MXsOu25forY1-kb35oQ9-Z5sLclbZJvrJ3x6Tz_u7j9kjnb88PM1u5nTFCxio4spiybQqtaq45ii9BJ00KLRUIKS0wHMBlXeOVQq9dK4SpUBZYsm1xjG5-r27sdHZpupt60I0mz6sbf9teI48PZhi099YTE5b-96UXfcVDQOzB2vQJEzmwMkcwOIPfNJlkQ</recordid><startdate>19970101</startdate><enddate>19970101</enddate><creator>Portinale, Luigi</creator><creator>Torasso, Pietro</creator><creator>Magro, Diego</creator><general>Springer Berlin Heidelberg</general><general>Springer-Verlag</general><scope>IQODW</scope></search><sort><creationdate>19970101</creationdate><title>Selecting most adaptable diagnostic solutions through Pivoting-Based Retrieval</title><author>Portinale, Luigi ; Torasso, Pietro ; Magro, Diego</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-j290t-626a3b186b86f28235e5086a3098560455a02740fecc1f63e5ccf4b435b3b2883</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1997</creationdate><topic>Adaptation Effort</topic><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Case Memory</topic><topic>Computer science; control theory; systems</topic><topic>Exact sciences and technology</topic><topic>Heuristic Estimate</topic><topic>Input Case</topic><topic>Learning and adaptive systems</topic><topic>Retrieval Time</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Portinale, Luigi</creatorcontrib><creatorcontrib>Torasso, Pietro</creatorcontrib><creatorcontrib>Magro, Diego</creatorcontrib><collection>Pascal-Francis</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Portinale, Luigi</au><au>Torasso, Pietro</au><au>Magro, Diego</au><au>Leake, David B.</au><au>Plaza, Enric</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Selecting most adaptable diagnostic solutions through Pivoting-Based Retrieval</atitle><btitle>Case-Based Reasoning Research and Development</btitle><date>1997-01-01</date><risdate>1997</risdate><spage>393</spage><epage>402</epage><pages>393-402</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>9783540632337</isbn><isbn>3540632336</isbn><eisbn>9783540692386</eisbn><eisbn>354069238X</eisbn><abstract>The aim of the present paper is to investigate a retrieval strategy for case-based diagnosis called Pivoting Based Retrieval (PBR), based on a tight integration between retrieval and adaptation estimation. It exploits a heuristic estimate of the adaptability of a solution; during retrieval, lower and upper bounds for such an estimate are computed for relevant cases and a pivot case is selected, determining which cases have to be considered and which have not. Such a technique has been evaluated on three different domain models and very satisfactory results have been obtained both in terms of accuracy, space and retrieval time</abstract><cop>Berlin, Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/3-540-63233-6_509</doi><tpages>10</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0302-9743 |
| ispartof | Case-Based Reasoning Research and Development, 1997, p.393-402 |
| issn | 0302-9743 1611-3349 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_2732217 |
| source | Springer Books |
| subjects | Adaptation Effort Applied sciences Artificial intelligence Case Memory Computer science control theory systems Exact sciences and technology Heuristic Estimate Input Case Learning and adaptive systems Retrieval Time |
| title | Selecting most adaptable diagnostic solutions through Pivoting-Based Retrieval |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-10T14%3A31%3A57IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-pascalfrancis_sprin&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Selecting%20most%20adaptable%20diagnostic%20solutions%20through%20Pivoting-Based%20Retrieval&rft.btitle=Case-Based%20Reasoning%20Research%20and%20Development&rft.au=Portinale,%20Luigi&rft.date=1997-01-01&rft.spage=393&rft.epage=402&rft.pages=393-402&rft.issn=0302-9743&rft.eissn=1611-3349&rft.isbn=9783540632337&rft.isbn_list=3540632336&rft_id=info:doi/10.1007/3-540-63233-6_509&rft_dat=%3Cpascalfrancis_sprin%3E2732217%3C/pascalfrancis_sprin%3E%3Curl%3E%3C/url%3E&rft.eisbn=9783540692386&rft.eisbn_list=354069238X&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true |