Effective learning in dynamic environments by explicit context tracking View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

1993

AUTHORS

Gerhard Widmer , Miroslav Kubat

ABSTRACT

Daily experience shows that in the real world, the meaning of many concepts heavily depends on some implicit context, and changes in that context can cause radical changes in the concepts. This paper introduces a method for incremental concept learning in dynamic environments where the target concepts may be context-dependent and may change drastically over time. The method has been implemented in a system called FLORA3. FLORA3 is very flexible in adapting to changes in the target concepts and tracking concept drift. Moreover, by explicitly storing old hypotheses and re-using them to bias learning in new contexts, it possesses the ability to utilize experience from previous learning. This greatly increases the system's effectiveness in environments where contexts can reoccur periodically. The paper describes the various algorithms that constitute the method and reports on several experiments that demonstrate the flexibility of FLORA3 in dynamic environments. More... »

PAGES

227-243

References to SciGraph publications

Book

TITLE

Machine Learning: ECML-93

ISBN

978-3-540-56602-1
978-3-540-47597-2

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/3-540-56602-3_139

DOI

http://dx.doi.org/10.1007/3-540-56602-3_139

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1036804921


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/2008/1701", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Psychology", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/17", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Psychology and Cognitive Sciences", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "University of Vienna", 
          "id": "https://www.grid.ac/institutes/grid.10420.37", 
          "name": [
            "Dept. of Medical Cybernetics and Artificial Intelligence, University of 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": "Graz University of Technology", 
          "id": "https://www.grid.ac/institutes/grid.410413.3", 
          "name": [
            "Institute of Biomedical Engineering, Dept. for Medical Informatics, Graz University of Technology, Brockmanngasse 41, A-8010\u00a0Graz, Austria"
          ], 
          "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": "https://doi.org/10.1016/0004-3702(89)90046-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004978830"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0004-3702(89)90046-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004978830"
        ], 
        "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/bf00116835", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013474790", 
          "https://doi.org/10.1007/bf00116835"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00116835", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013474790", 
          "https://doi.org/10.1007/bf00116835"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00114265", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017000685", 
          "https://doi.org/10.1007/bf00114265"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00114265", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017000685", 
          "https://doi.org/10.1007/bf00114265"
        ], 
        "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/bfb0017020", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033013144", 
          "https://doi.org/10.1007/bfb0017020"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0167-8655(89)90092-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044019720"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "1993", 
    "datePublishedReg": "1993-01-01", 
    "description": "Daily experience shows that in the real world, the meaning of many concepts heavily depends on some implicit context, and changes in that context can cause radical changes in the concepts. This paper introduces a method for incremental concept learning in dynamic environments where the target concepts may be context-dependent and may change drastically over time. The method has been implemented in a system called FLORA3. FLORA3 is very flexible in adapting to changes in the target concepts and tracking concept drift. Moreover, by explicitly storing old hypotheses and re-using them to bias learning in new contexts, it possesses the ability to utilize experience from previous learning. This greatly increases the system's effectiveness in environments where contexts can reoccur periodically. The paper describes the various algorithms that constitute the method and reports on several experiments that demonstrate the flexibility of FLORA3 in dynamic environments.", 
    "editor": [
      {
        "familyName": "Brazdil", 
        "givenName": "Pavel B.", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/3-540-56602-3_139", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": {
      "isbn": [
        "978-3-540-56602-1", 
        "978-3-540-47597-2"
      ], 
      "name": "Machine Learning: ECML-93", 
      "type": "Book"
    }, 
    "name": "Effective learning in dynamic environments by explicit context tracking", 
    "pagination": "227-243", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/3-540-56602-3_139"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "af5345d90c3eef50f215bfbd88ae6f1d07a11bcf48d1c8370f64efa17d50ac96"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1036804921"
        ]
      }
    ], 
    "publisher": {
      "location": "Berlin, Heidelberg", 
      "name": "Springer Berlin Heidelberg", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/3-540-56602-3_139", 
      "https://app.dimensions.ai/details/publication/pub.1036804921"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-15T14:13", 
    "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/v90/0000000001_0000000264/records_8669_00000063.jsonl", 
    "type": "Chapter", 
    "url": "http://link.springer.com/10.1007/3-540-56602-3_139"
  }
]
 

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.10.1007/3-540-56602-3_139'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/3-540-56602-3_139'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/3-540-56602-3_139'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/3-540-56602-3_139'


 

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

103 TRIPLES      23 PREDICATES      35 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/3-540-56602-3_139 schema:about anzsrc-for:17
2 anzsrc-for:1701
3 schema:author Nd86095926e8240e3a235204ec619d9ca
4 schema:citation sg:pub.10.1007/3-540-56602-3_140
5 sg:pub.10.1007/bf00114265
6 sg:pub.10.1007/bf00116835
7 sg:pub.10.1007/bfb0017020
8 https://doi.org/10.1016/0004-3702(89)90046-5
9 https://doi.org/10.1016/0004-3702(91)90041-h
10 https://doi.org/10.1016/0167-8655(89)90092-5
11 https://doi.org/10.1080/01969729208927471
12 schema:datePublished 1993
13 schema:datePublishedReg 1993-01-01
14 schema:description Daily experience shows that in the real world, the meaning of many concepts heavily depends on some implicit context, and changes in that context can cause radical changes in the concepts. This paper introduces a method for incremental concept learning in dynamic environments where the target concepts may be context-dependent and may change drastically over time. The method has been implemented in a system called FLORA3. FLORA3 is very flexible in adapting to changes in the target concepts and tracking concept drift. Moreover, by explicitly storing old hypotheses and re-using them to bias learning in new contexts, it possesses the ability to utilize experience from previous learning. This greatly increases the system's effectiveness in environments where contexts can reoccur periodically. The paper describes the various algorithms that constitute the method and reports on several experiments that demonstrate the flexibility of FLORA3 in dynamic environments.
15 schema:editor Ne8015392bae545ce8ef424455f0d7b2d
16 schema:genre chapter
17 schema:inLanguage en
18 schema:isAccessibleForFree true
19 schema:isPartOf N4839282343a84093abab69e6dc7207ec
20 schema:name Effective learning in dynamic environments by explicit context tracking
21 schema:pagination 227-243
22 schema:productId N4deb0f4c9bf04dd686d2cb7fe345f77c
23 N59ddc5d015db4103a134c30500922f74
24 Na9f09faf862a448eba89bd0f013208af
25 schema:publisher N543d724c26234dbda5cfda8d397d31f1
26 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036804921
27 https://doi.org/10.1007/3-540-56602-3_139
28 schema:sdDatePublished 2019-04-15T14:13
29 schema:sdLicense https://scigraph.springernature.com/explorer/license/
30 schema:sdPublisher Necf0732747534b8389d4470074adcc16
31 schema:url http://link.springer.com/10.1007/3-540-56602-3_139
32 sgo:license sg:explorer/license/
33 sgo:sdDataset chapters
34 rdf:type schema:Chapter
35 N4839282343a84093abab69e6dc7207ec schema:isbn 978-3-540-47597-2
36 978-3-540-56602-1
37 schema:name Machine Learning: ECML-93
38 rdf:type schema:Book
39 N4deb0f4c9bf04dd686d2cb7fe345f77c schema:name doi
40 schema:value 10.1007/3-540-56602-3_139
41 rdf:type schema:PropertyValue
42 N543d724c26234dbda5cfda8d397d31f1 schema:location Berlin, Heidelberg
43 schema:name Springer Berlin Heidelberg
44 rdf:type schema:Organisation
45 N59ddc5d015db4103a134c30500922f74 schema:name dimensions_id
46 schema:value pub.1036804921
47 rdf:type schema:PropertyValue
48 Na9f09faf862a448eba89bd0f013208af schema:name readcube_id
49 schema:value af5345d90c3eef50f215bfbd88ae6f1d07a11bcf48d1c8370f64efa17d50ac96
50 rdf:type schema:PropertyValue
51 Nbfd58f0e466b4636a63fc6b39e1f79be schema:familyName Brazdil
52 schema:givenName Pavel B.
53 rdf:type schema:Person
54 Nd86095926e8240e3a235204ec619d9ca rdf:first sg:person.013641401431.40
55 rdf:rest Ne91215ccbed34694b80afec5a7a41c60
56 Ne8015392bae545ce8ef424455f0d7b2d rdf:first Nbfd58f0e466b4636a63fc6b39e1f79be
57 rdf:rest rdf:nil
58 Ne91215ccbed34694b80afec5a7a41c60 rdf:first sg:person.016461026707.02
59 rdf:rest rdf:nil
60 Necf0732747534b8389d4470074adcc16 schema:name Springer Nature - SN SciGraph project
61 rdf:type schema:Organization
62 anzsrc-for:17 schema:inDefinedTermSet anzsrc-for:
63 schema:name Psychology and Cognitive Sciences
64 rdf:type schema:DefinedTerm
65 anzsrc-for:1701 schema:inDefinedTermSet anzsrc-for:
66 schema:name Psychology
67 rdf:type schema:DefinedTerm
68 sg:person.013641401431.40 schema:affiliation https://www.grid.ac/institutes/grid.10420.37
69 schema:familyName Widmer
70 schema:givenName Gerhard
71 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013641401431.40
72 rdf:type schema:Person
73 sg:person.016461026707.02 schema:affiliation https://www.grid.ac/institutes/grid.410413.3
74 schema:familyName Kubat
75 schema:givenName Miroslav
76 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016461026707.02
77 rdf:type schema:Person
78 sg:pub.10.1007/3-540-56602-3_140 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019860602
79 https://doi.org/10.1007/3-540-56602-3_140
80 rdf:type schema:CreativeWork
81 sg:pub.10.1007/bf00114265 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017000685
82 https://doi.org/10.1007/bf00114265
83 rdf:type schema:CreativeWork
84 sg:pub.10.1007/bf00116835 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013474790
85 https://doi.org/10.1007/bf00116835
86 rdf:type schema:CreativeWork
87 sg:pub.10.1007/bfb0017020 schema:sameAs https://app.dimensions.ai/details/publication/pub.1033013144
88 https://doi.org/10.1007/bfb0017020
89 rdf:type schema:CreativeWork
90 https://doi.org/10.1016/0004-3702(89)90046-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004978830
91 rdf:type schema:CreativeWork
92 https://doi.org/10.1016/0004-3702(91)90041-h schema:sameAs https://app.dimensions.ai/details/publication/pub.1007932787
93 rdf:type schema:CreativeWork
94 https://doi.org/10.1016/0167-8655(89)90092-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044019720
95 rdf:type schema:CreativeWork
96 https://doi.org/10.1080/01969729208927471 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009759642
97 rdf:type schema:CreativeWork
98 https://www.grid.ac/institutes/grid.10420.37 schema:alternateName University of Vienna
99 schema:name Dept. of Medical Cybernetics and Artificial Intelligence, University of Vienna, Austria
100 rdf:type schema:Organization
101 https://www.grid.ac/institutes/grid.410413.3 schema:alternateName Graz University of Technology
102 schema:name Institute of Biomedical Engineering, Dept. for Medical Informatics, Graz University of Technology, Brockmanngasse 41, A-8010 Graz, Austria
103 rdf:type schema:Organization
 




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


...