Text categorization with Support Vector Machines: Learning with many relevant features View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

1998

AUTHORS

Thorsten Joachims

ABSTRACT

This paper explores the use of Support Vector Machines (SVMs) for learning text classifiers from examples. It analyzes the particular properties of learning with text data and identifies why SVMs are appropriate for this task. Empirical results support the theoretical findings. SVMs achieve substantial improvements over the currently best performing methods and behave robustly over a variety of different learning tasks. Furthermore they are fully automatic, eliminating the need for manual parameter tuning. More... »

PAGES

137-142

Book

TITLE

Machine Learning: ECML-98

ISBN

978-3-540-64417-0
978-3-540-69781-7

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bfb0026683

DOI

http://dx.doi.org/10.1007/bfb0026683

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1051853845


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": "TU Dortmund University", 
          "id": "https://www.grid.ac/institutes/grid.5675.1", 
          "name": [
            "Universit\u00e4t Dortmund, Inforinatik LS8, Baroper Str. 301, 44221\u00a0Dortmund, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Joachims", 
        "givenName": "Thorsten", 
        "id": "sg:person.01316712343.43", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01316712343.43"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1016/0306-4573(88)90021-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032478827"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0306-4573(88)90021-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032478827"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "1998", 
    "datePublishedReg": "1998-01-01", 
    "description": "This paper explores the use of Support Vector Machines (SVMs) for learning text classifiers from examples. It analyzes the particular properties of learning with text data and identifies why SVMs are appropriate for this task. Empirical results support the theoretical findings. SVMs achieve substantial improvements over the currently best performing methods and behave robustly over a variety of different learning tasks. Furthermore they are fully automatic, eliminating the need for manual parameter tuning.", 
    "editor": [
      {
        "familyName": "N\u00e9dellec", 
        "givenName": "Claire", 
        "type": "Person"
      }, 
      {
        "familyName": "Rouveirol", 
        "givenName": "C\u00e9line", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/bfb0026683", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": true, 
    "isPartOf": {
      "isbn": [
        "978-3-540-64417-0", 
        "978-3-540-69781-7"
      ], 
      "name": "Machine Learning: ECML-98", 
      "type": "Book"
    }, 
    "name": "Text categorization with Support Vector Machines: Learning with many relevant features", 
    "pagination": "137-142", 
    "productId": [
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/bfb0026683"
        ]
      }, 
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "f700af67e1f1df10e25cee6e0010944878a2a6320536fce3d75f0eb6b4b1482b"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1051853845"
        ]
      }
    ], 
    "publisher": {
      "location": "Berlin, Heidelberg", 
      "name": "Springer Berlin Heidelberg", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/bfb0026683", 
      "https://app.dimensions.ai/details/publication/pub.1051853845"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2019-04-15T13:31", 
    "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_8664_00000275.jsonl", 
    "type": "Chapter", 
    "url": "http://link.springer.com/10.1007/BFb0026683"
  }
]
 

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/bfb0026683'

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/bfb0026683'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/bfb0026683'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/bfb0026683'


 

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

73 TRIPLES      23 PREDICATES      28 URIs      20 LITERALS      8 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/bfb0026683 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author Nd29fcbb0fc01460eb3ac3da3a2ebd37d
4 schema:citation https://doi.org/10.1016/0306-4573(88)90021-0
5 schema:datePublished 1998
6 schema:datePublishedReg 1998-01-01
7 schema:description This paper explores the use of Support Vector Machines (SVMs) for learning text classifiers from examples. It analyzes the particular properties of learning with text data and identifies why SVMs are appropriate for this task. Empirical results support the theoretical findings. SVMs achieve substantial improvements over the currently best performing methods and behave robustly over a variety of different learning tasks. Furthermore they are fully automatic, eliminating the need for manual parameter tuning.
8 schema:editor N10e6f6ff32f94e12ae77e966324ef642
9 schema:genre chapter
10 schema:inLanguage en
11 schema:isAccessibleForFree true
12 schema:isPartOf Na483662b30de43b091fabb1e516beadf
13 schema:name Text categorization with Support Vector Machines: Learning with many relevant features
14 schema:pagination 137-142
15 schema:productId N7268ff4d5a4849f481ef51072694b70a
16 N76a65b9c45ca46fe9fc64902310a2ec7
17 Na4161aac5f9e45b08aff93fddb599414
18 schema:publisher N6379b2841b4f43119ef1b62c1aaf6e0d
19 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051853845
20 https://doi.org/10.1007/bfb0026683
21 schema:sdDatePublished 2019-04-15T13:31
22 schema:sdLicense https://scigraph.springernature.com/explorer/license/
23 schema:sdPublisher N91315ca4af554855b219b2f81dc32b79
24 schema:url http://link.springer.com/10.1007/BFb0026683
25 sgo:license sg:explorer/license/
26 sgo:sdDataset chapters
27 rdf:type schema:Chapter
28 N10e6f6ff32f94e12ae77e966324ef642 rdf:first N7e07d83b289f413fafa9278b0e909c8d
29 rdf:rest N2ad2ef5d93564437aedf5879116a8be8
30 N2ad2ef5d93564437aedf5879116a8be8 rdf:first Nc3f2709f9f06402ca2a68fd5da98a3e7
31 rdf:rest rdf:nil
32 N6379b2841b4f43119ef1b62c1aaf6e0d schema:location Berlin, Heidelberg
33 schema:name Springer Berlin Heidelberg
34 rdf:type schema:Organisation
35 N7268ff4d5a4849f481ef51072694b70a schema:name doi
36 schema:value 10.1007/bfb0026683
37 rdf:type schema:PropertyValue
38 N76a65b9c45ca46fe9fc64902310a2ec7 schema:name dimensions_id
39 schema:value pub.1051853845
40 rdf:type schema:PropertyValue
41 N7e07d83b289f413fafa9278b0e909c8d schema:familyName Nédellec
42 schema:givenName Claire
43 rdf:type schema:Person
44 N91315ca4af554855b219b2f81dc32b79 schema:name Springer Nature - SN SciGraph project
45 rdf:type schema:Organization
46 Na4161aac5f9e45b08aff93fddb599414 schema:name readcube_id
47 schema:value f700af67e1f1df10e25cee6e0010944878a2a6320536fce3d75f0eb6b4b1482b
48 rdf:type schema:PropertyValue
49 Na483662b30de43b091fabb1e516beadf schema:isbn 978-3-540-64417-0
50 978-3-540-69781-7
51 schema:name Machine Learning: ECML-98
52 rdf:type schema:Book
53 Nc3f2709f9f06402ca2a68fd5da98a3e7 schema:familyName Rouveirol
54 schema:givenName Céline
55 rdf:type schema:Person
56 Nd29fcbb0fc01460eb3ac3da3a2ebd37d rdf:first sg:person.01316712343.43
57 rdf:rest rdf:nil
58 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
59 schema:name Information and Computing Sciences
60 rdf:type schema:DefinedTerm
61 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
62 schema:name Artificial Intelligence and Image Processing
63 rdf:type schema:DefinedTerm
64 sg:person.01316712343.43 schema:affiliation https://www.grid.ac/institutes/grid.5675.1
65 schema:familyName Joachims
66 schema:givenName Thorsten
67 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01316712343.43
68 rdf:type schema:Person
69 https://doi.org/10.1016/0306-4573(88)90021-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032478827
70 rdf:type schema:CreativeWork
71 https://www.grid.ac/institutes/grid.5675.1 schema:alternateName TU Dortmund University
72 schema:name Universität Dortmund, Inforinatik LS8, Baroper Str. 301, 44221 Dortmund, Germany
73 rdf:type schema:Organization
 




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


...