Learning in the presence of concept drift and hidden contexts View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

1996-04

AUTHORS

Gerhard Widmer, Miroslav Kubat

ABSTRACT

On-line learning in domains where the target concept depends on some hidden context poses serious problems. A changing context can induce changes in the target concepts, producing what is known as concept drift. We describe a family of learning algorithms that flexibly react to concept drift and can take advantage of situations where contexts reappear. The general approach underlying all these algorithms consists of (1) keeping only a window of currently trusted examples and hypotheses; (2) storing concept descriptions and reusing them when a previous context re-appears; and (3) controlling both of these functions by a heuristic that constantly monitors the system's behavior. The paper reports on experiments that test the systems' perfomance under various conditions such as different levels of noise and different extent and rate of concept drift. More... »

PAGES

69-101

References to SciGraph publications

  • 1982-10. Rough sets in INTERNATIONAL JOURNAL OF PARALLEL PROGRAMMING
  • 1991-01. Instance-based learning algorithms in MACHINE LEARNING
  • 1993. Effective learning in dynamic environments by explicit context tracking in MACHINE LEARNING: ECML-93
  • 1988-04. Queries and concept learning in MACHINE LEARNING
  • 1991-05. A nearest hyperrectangle learning method in MACHINE LEARNING
  • 1986-09. Incremental learning from noisy data in MACHINE LEARNING
  • 1983. A Theory and Methodology of Inductive Learning in MACHINE LEARNING
  • 1994-01. Tracking drifting concepts by minimizing disagreements in MACHINE LEARNING
  • 1993-10. Flexible concept learning in real-time systems in JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS
  • 1994-06. Associative reinforcement learning: Functions ink-DNF in MACHINE LEARNING
  • 1993-01. A weighted nearest neighbor algorithm for learning with symbolic features in MACHINE LEARNING
  • 1986-03. Explanation-based generalization: A unifying view in MACHINE LEARNING
  • 1987-09. Experiments with incremental concept formation: UNIMEM in MACHINE LEARNING
  • 1983. Machine Learning, An Artificial Intelligence Approach in NONE
  • 1993. COBBIT—A control procedure for COBWEB in the presence of concept drift in MACHINE LEARNING: ECML-93
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/bf00116900

    DOI

    http://dx.doi.org/10.1007/bf00116900

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1025237168


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Artificial Intelligence and Image Processing", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information and Computing Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "University of Vienna", 
              "id": "https://www.grid.ac/institutes/grid.10420.37", 
              "name": [
                "Department of Medical Cybernetics and AI, University of Vienna Intelligence, Schottengasse 3, A-1010, Vienna, Austria", 
                "Austrian Research Institute for Artificial, Schottengasse 3, A-1010, Vienna, Austria"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Widmer", 
            "givenName": "Gerhard", 
            "id": "sg:person.013641401431.40", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013641401431.40"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "University of Ottawa", 
              "id": "https://www.grid.ac/institutes/grid.28046.38", 
              "name": [
                "Department of Computer Science, University of Ottawa, 150 Louis Pasteur, KIN 6N5, Ottawa, Ontario, Canada"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Kubat", 
            "givenName": "Miroslav", 
            "id": "sg:person.016461026707.02", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016461026707.02"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "sg:pub.10.1007/bf00114264", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000846536", 
              "https://doi.org/10.1007/bf00114264"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00114264", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000846536", 
              "https://doi.org/10.1007/bf00114264"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf01257993", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003822618", 
              "https://doi.org/10.1007/bf01257993"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf01257993", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003822618", 
              "https://doi.org/10.1007/bf01257993"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/76359.76371", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004063675"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-662-12405-5_4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004370089", 
              "https://doi.org/10.1007/978-3-662-12405-5_4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0004-3702(91)90041-h", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1007932787"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0004-3702(91)90041-h", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1007932787"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1080/01969729208927471", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009759642"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00114779", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016646699", 
              "https://doi.org/10.1007/bf00114779"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00114779", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1016646699", 
              "https://doi.org/10.1007/bf00114779"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00116250", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1017707493", 
              "https://doi.org/10.1007/bf00116250"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00116250", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1017707493", 
              "https://doi.org/10.1007/bf00116250"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/b978-1-55860-307-3.50042-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1018885226"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/3-540-56602-3_140", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019860602", 
              "https://doi.org/10.1007/3-540-56602-3_140"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf01001956", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020579132", 
              "https://doi.org/10.1007/bf01001956"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/b978-1-55860-213-7.50006-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1021135600"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1111/j.1551-6708.1987.tb00862.x", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027181663"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00993481", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028790090", 
              "https://doi.org/10.1007/bf00993481"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00116895", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029305571", 
              "https://doi.org/10.1007/bf00116895"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00116895", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029305571", 
              "https://doi.org/10.1007/bf00116895"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00116828", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030500577", 
              "https://doi.org/10.1007/bf00116828"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00116828", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030500577", 
              "https://doi.org/10.1007/bf00116828"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00993347", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032410020", 
              "https://doi.org/10.1007/bf00993347"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/b978-0-934613-64-4.50051-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032689821"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0167-8655(92)90110-l", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033409773"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0167-8655(92)90110-l", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1033409773"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/b978-0-934613-64-4.50052-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1034475197"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-662-12405-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036581552", 
              "https://doi.org/10.1007/978-3-662-12405-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-662-12405-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036581552", 
              "https://doi.org/10.1007/978-3-662-12405-5"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/3-540-56602-3_139", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036804921", 
              "https://doi.org/10.1007/3-540-56602-3_139"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/1968.1972", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1038881641"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/b978-1-55860-213-7.50019-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1040291799"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/b978-0-934613-41-5.50035-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1042854058"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0167-8655(89)90092-5", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1044019720"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00993161", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1049073428", 
              "https://doi.org/10.1007/bf00993161"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00153759", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1049631378", 
              "https://doi.org/10.1007/bf00153759"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/bf00153759", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1049631378", 
              "https://doi.org/10.1007/bf00153759"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/sfcs.1992.267802", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1086354740"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "1996-04", 
        "datePublishedReg": "1996-04-01", 
        "description": "On-line learning in domains where the target concept depends on some hidden context poses serious problems. A changing context can induce changes in the target concepts, producing what is known as concept drift. We describe a family of learning algorithms that flexibly react to concept drift and can take advantage of situations where contexts reappear. The general approach underlying all these algorithms consists of (1) keeping only a window of currently trusted examples and hypotheses; (2) storing concept descriptions and reusing them when a previous context re-appears; and (3) controlling both of these functions by a heuristic that constantly monitors the system's behavior. The paper reports on experiments that test the systems' perfomance under various conditions such as different levels of noise and different extent and rate of concept drift.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/bf00116900", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": true, 
        "isPartOf": [
          {
            "id": "sg:journal.1125588", 
            "issn": [
              "0885-6125", 
              "1573-0565"
            ], 
            "name": "Machine Learning", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "1", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "23"
          }
        ], 
        "name": "Learning in the presence of concept drift and hidden contexts", 
        "pagination": "69-101", 
        "productId": [
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/bf00116900"
            ]
          }, 
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "783ad63907c13ff82d3fecbc0f876949a199edf4ce7baa588971e95773e268ff"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1025237168"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/bf00116900", 
          "https://app.dimensions.ai/details/publication/pub.1025237168"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-15T08:47", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000374_0000000374/records_119713_00000000.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "http://link.springer.com/10.1007/BF00116900"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/bf00116900'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/bf00116900'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/bf00116900'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/bf00116900'


     

    This table displays all metadata directly associated to this object as RDF triples.

    174 TRIPLES      21 PREDICATES      56 URIs      19 LITERALS      7 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/bf00116900 schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author N2772746a77f343acba14b9c0f5b72eda
    4 schema:citation sg:pub.10.1007/3-540-56602-3_139
    5 sg:pub.10.1007/3-540-56602-3_140
    6 sg:pub.10.1007/978-3-662-12405-5
    7 sg:pub.10.1007/978-3-662-12405-5_4
    8 sg:pub.10.1007/bf00114264
    9 sg:pub.10.1007/bf00114779
    10 sg:pub.10.1007/bf00116250
    11 sg:pub.10.1007/bf00116828
    12 sg:pub.10.1007/bf00116895
    13 sg:pub.10.1007/bf00153759
    14 sg:pub.10.1007/bf00993161
    15 sg:pub.10.1007/bf00993347
    16 sg:pub.10.1007/bf00993481
    17 sg:pub.10.1007/bf01001956
    18 sg:pub.10.1007/bf01257993
    19 https://doi.org/10.1016/0004-3702(91)90041-h
    20 https://doi.org/10.1016/0167-8655(89)90092-5
    21 https://doi.org/10.1016/0167-8655(92)90110-l
    22 https://doi.org/10.1016/b978-0-934613-41-5.50035-0
    23 https://doi.org/10.1016/b978-0-934613-64-4.50051-7
    24 https://doi.org/10.1016/b978-0-934613-64-4.50052-9
    25 https://doi.org/10.1016/b978-1-55860-213-7.50006-7
    26 https://doi.org/10.1016/b978-1-55860-213-7.50019-5
    27 https://doi.org/10.1016/b978-1-55860-307-3.50042-3
    28 https://doi.org/10.1080/01969729208927471
    29 https://doi.org/10.1109/sfcs.1992.267802
    30 https://doi.org/10.1111/j.1551-6708.1987.tb00862.x
    31 https://doi.org/10.1145/1968.1972
    32 https://doi.org/10.1145/76359.76371
    33 schema:datePublished 1996-04
    34 schema:datePublishedReg 1996-04-01
    35 schema:description On-line learning in domains where the target concept depends on some hidden context poses serious problems. A changing context can induce changes in the target concepts, producing what is known as concept drift. We describe a family of learning algorithms that flexibly react to concept drift and can take advantage of situations where contexts reappear. The general approach underlying all these algorithms consists of (1) keeping only a window of currently trusted examples and hypotheses; (2) storing concept descriptions and reusing them when a previous context re-appears; and (3) controlling both of these functions by a heuristic that constantly monitors the system's behavior. The paper reports on experiments that test the systems' perfomance under various conditions such as different levels of noise and different extent and rate of concept drift.
    36 schema:genre research_article
    37 schema:inLanguage en
    38 schema:isAccessibleForFree true
    39 schema:isPartOf N895841944c134ad698161c84a309856f
    40 Nd0884cc4e5864947880f98e8eac4488d
    41 sg:journal.1125588
    42 schema:name Learning in the presence of concept drift and hidden contexts
    43 schema:pagination 69-101
    44 schema:productId N04a18c38b09343b99581c310c5b0fc34
    45 N48108a18409544c1b3461765b0fbf13b
    46 N52b2c056207d4ad7b17de5306c3889eb
    47 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025237168
    48 https://doi.org/10.1007/bf00116900
    49 schema:sdDatePublished 2019-04-15T08:47
    50 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    51 schema:sdPublisher N68a89581c9b54aa08e92c0f9dc970468
    52 schema:url http://link.springer.com/10.1007/BF00116900
    53 sgo:license sg:explorer/license/
    54 sgo:sdDataset articles
    55 rdf:type schema:ScholarlyArticle
    56 N04a18c38b09343b99581c310c5b0fc34 schema:name readcube_id
    57 schema:value 783ad63907c13ff82d3fecbc0f876949a199edf4ce7baa588971e95773e268ff
    58 rdf:type schema:PropertyValue
    59 N2772746a77f343acba14b9c0f5b72eda rdf:first sg:person.013641401431.40
    60 rdf:rest N437e9d4e1b8c461da745e45cfffaa0de
    61 N437e9d4e1b8c461da745e45cfffaa0de rdf:first sg:person.016461026707.02
    62 rdf:rest rdf:nil
    63 N48108a18409544c1b3461765b0fbf13b schema:name dimensions_id
    64 schema:value pub.1025237168
    65 rdf:type schema:PropertyValue
    66 N52b2c056207d4ad7b17de5306c3889eb schema:name doi
    67 schema:value 10.1007/bf00116900
    68 rdf:type schema:PropertyValue
    69 N68a89581c9b54aa08e92c0f9dc970468 schema:name Springer Nature - SN SciGraph project
    70 rdf:type schema:Organization
    71 N895841944c134ad698161c84a309856f schema:issueNumber 1
    72 rdf:type schema:PublicationIssue
    73 Nd0884cc4e5864947880f98e8eac4488d schema:volumeNumber 23
    74 rdf:type schema:PublicationVolume
    75 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    76 schema:name Information and Computing Sciences
    77 rdf:type schema:DefinedTerm
    78 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    79 schema:name Artificial Intelligence and Image Processing
    80 rdf:type schema:DefinedTerm
    81 sg:journal.1125588 schema:issn 0885-6125
    82 1573-0565
    83 schema:name Machine Learning
    84 rdf:type schema:Periodical
    85 sg:person.013641401431.40 schema:affiliation https://www.grid.ac/institutes/grid.10420.37
    86 schema:familyName Widmer
    87 schema:givenName Gerhard
    88 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013641401431.40
    89 rdf:type schema:Person
    90 sg:person.016461026707.02 schema:affiliation https://www.grid.ac/institutes/grid.28046.38
    91 schema:familyName Kubat
    92 schema:givenName Miroslav
    93 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016461026707.02
    94 rdf:type schema:Person
    95 sg:pub.10.1007/3-540-56602-3_139 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036804921
    96 https://doi.org/10.1007/3-540-56602-3_139
    97 rdf:type schema:CreativeWork
    98 sg:pub.10.1007/3-540-56602-3_140 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019860602
    99 https://doi.org/10.1007/3-540-56602-3_140
    100 rdf:type schema:CreativeWork
    101 sg:pub.10.1007/978-3-662-12405-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036581552
    102 https://doi.org/10.1007/978-3-662-12405-5
    103 rdf:type schema:CreativeWork
    104 sg:pub.10.1007/978-3-662-12405-5_4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004370089
    105 https://doi.org/10.1007/978-3-662-12405-5_4
    106 rdf:type schema:CreativeWork
    107 sg:pub.10.1007/bf00114264 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000846536
    108 https://doi.org/10.1007/bf00114264
    109 rdf:type schema:CreativeWork
    110 sg:pub.10.1007/bf00114779 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016646699
    111 https://doi.org/10.1007/bf00114779
    112 rdf:type schema:CreativeWork
    113 sg:pub.10.1007/bf00116250 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017707493
    114 https://doi.org/10.1007/bf00116250
    115 rdf:type schema:CreativeWork
    116 sg:pub.10.1007/bf00116828 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030500577
    117 https://doi.org/10.1007/bf00116828
    118 rdf:type schema:CreativeWork
    119 sg:pub.10.1007/bf00116895 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029305571
    120 https://doi.org/10.1007/bf00116895
    121 rdf:type schema:CreativeWork
    122 sg:pub.10.1007/bf00153759 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049631378
    123 https://doi.org/10.1007/bf00153759
    124 rdf:type schema:CreativeWork
    125 sg:pub.10.1007/bf00993161 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049073428
    126 https://doi.org/10.1007/bf00993161
    127 rdf:type schema:CreativeWork
    128 sg:pub.10.1007/bf00993347 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032410020
    129 https://doi.org/10.1007/bf00993347
    130 rdf:type schema:CreativeWork
    131 sg:pub.10.1007/bf00993481 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028790090
    132 https://doi.org/10.1007/bf00993481
    133 rdf:type schema:CreativeWork
    134 sg:pub.10.1007/bf01001956 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020579132
    135 https://doi.org/10.1007/bf01001956
    136 rdf:type schema:CreativeWork
    137 sg:pub.10.1007/bf01257993 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003822618
    138 https://doi.org/10.1007/bf01257993
    139 rdf:type schema:CreativeWork
    140 https://doi.org/10.1016/0004-3702(91)90041-h schema:sameAs https://app.dimensions.ai/details/publication/pub.1007932787
    141 rdf:type schema:CreativeWork
    142 https://doi.org/10.1016/0167-8655(89)90092-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044019720
    143 rdf:type schema:CreativeWork
    144 https://doi.org/10.1016/0167-8655(92)90110-l schema:sameAs https://app.dimensions.ai/details/publication/pub.1033409773
    145 rdf:type schema:CreativeWork
    146 https://doi.org/10.1016/b978-0-934613-41-5.50035-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042854058
    147 rdf:type schema:CreativeWork
    148 https://doi.org/10.1016/b978-0-934613-64-4.50051-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032689821
    149 rdf:type schema:CreativeWork
    150 https://doi.org/10.1016/b978-0-934613-64-4.50052-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034475197
    151 rdf:type schema:CreativeWork
    152 https://doi.org/10.1016/b978-1-55860-213-7.50006-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021135600
    153 rdf:type schema:CreativeWork
    154 https://doi.org/10.1016/b978-1-55860-213-7.50019-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040291799
    155 rdf:type schema:CreativeWork
    156 https://doi.org/10.1016/b978-1-55860-307-3.50042-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018885226
    157 rdf:type schema:CreativeWork
    158 https://doi.org/10.1080/01969729208927471 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009759642
    159 rdf:type schema:CreativeWork
    160 https://doi.org/10.1109/sfcs.1992.267802 schema:sameAs https://app.dimensions.ai/details/publication/pub.1086354740
    161 rdf:type schema:CreativeWork
    162 https://doi.org/10.1111/j.1551-6708.1987.tb00862.x schema:sameAs https://app.dimensions.ai/details/publication/pub.1027181663
    163 rdf:type schema:CreativeWork
    164 https://doi.org/10.1145/1968.1972 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038881641
    165 rdf:type schema:CreativeWork
    166 https://doi.org/10.1145/76359.76371 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004063675
    167 rdf:type schema:CreativeWork
    168 https://www.grid.ac/institutes/grid.10420.37 schema:alternateName University of Vienna
    169 schema:name Austrian Research Institute for Artificial, Schottengasse 3, A-1010, Vienna, Austria
    170 Department of Medical Cybernetics and AI, University of Vienna Intelligence, Schottengasse 3, A-1010, Vienna, Austria
    171 rdf:type schema:Organization
    172 https://www.grid.ac/institutes/grid.28046.38 schema:alternateName University of Ottawa
    173 schema:name Department of Computer Science, University of Ottawa, 150 Louis Pasteur, KIN 6N5, Ottawa, Ontario, Canada
    174 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...