TS, a test-split algorithm for inductive learning:
Abstract: "This paper presents a new attribute-based learning algorithm, TS. Different from ID3, AQ11 and HCV in strategies, this algorithm operates in cycles of test and split. It uses those attribute values which occur only in positive examples but not in negative examples to discriminate pos...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Edinburgh
1991
|
| Schriftenreihe: | University <Edinburgh> / Department of Artificial Intelligence: DAI research paper
568 |
| Schlagworte: | |
| Zusammenfassung: | Abstract: "This paper presents a new attribute-based learning algorithm, TS. Different from ID3, AQ11 and HCV in strategies, this algorithm operates in cycles of test and split. It uses those attribute values which occur only in positive examples but not in negative examples to discriminate positive examples against negative examples in a straight- forward manner and chooses the attributes with the least number of different values to split example sets. TS is natural, easy to implement, and polynomial in time complexity." |
| Beschreibung: | 13 S. |
Internformat
MARC
| LEADER | 00000nam a2200000 cb4500 | ||
|---|---|---|---|
| 001 | BV010459098 | ||
| 003 | DE-604 | ||
| 007 | t | ||
| 008 | 951031s1991 |||| 00||| engod | ||
| 035 | |a (OCoLC)1071638627 | ||
| 035 | |a (DE-599)BVBBV010459098 | ||
| 040 | |a DE-604 |b ger |e rakddb | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-91G | ||
| 100 | 1 | |a Wu, Xindong |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a TS, a test-split algorithm for inductive learning |
| 264 | 1 | |a Edinburgh |c 1991 | |
| 300 | |a 13 S. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a University <Edinburgh> / Department of Artificial Intelligence: DAI research paper |v 568 | |
| 520 | 3 | |a Abstract: "This paper presents a new attribute-based learning algorithm, TS. Different from ID3, AQ11 and HCV in strategies, this algorithm operates in cycles of test and split. It uses those attribute values which occur only in positive examples but not in negative examples to discriminate positive examples against negative examples in a straight- forward manner and chooses the attributes with the least number of different values to split example sets. TS is natural, easy to implement, and polynomial in time complexity." | |
| 650 | 7 | |a Bionics and artificial intelligence |2 sigle | |
| 650 | 7 | |a Computer software |2 sigle | |
| 650 | 4 | |a Machine learning | |
| 810 | 2 | |a Department of Artificial Intelligence: DAI research paper |t University <Edinburgh> |v 568 |w (DE-604)BV010450646 |9 568 | |
| 943 | 1 | |a oai:aleph.bib-bvb.de:BVB01-006968176 | |
Datensatz im Suchindex
| _version_ | 1812974139685732352 |
|---|---|
| adam_text | |
| any_adam_object | |
| author | Wu, Xindong |
| author_facet | Wu, Xindong |
| author_role | aut |
| author_sort | Wu, Xindong |
| author_variant | x w xw |
| building | Verbundindex |
| bvnumber | BV010459098 |
| ctrlnum | (OCoLC)1071638627 (DE-599)BVBBV010459098 |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>00000nam a2200000 cb4500</leader><controlfield tag="001">BV010459098</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">951031s1991 |||| 00||| engod</controlfield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1071638627</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV010459098</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rakddb</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-91G</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Wu, Xindong</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">TS, a test-split algorithm for inductive learning</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Edinburgh</subfield><subfield code="c">1991</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">13 S.</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="1" ind2=" "><subfield code="a">University <Edinburgh> / Department of Artificial Intelligence: DAI research paper</subfield><subfield code="v">568</subfield></datafield><datafield tag="520" ind1="3" ind2=" "><subfield code="a">Abstract: "This paper presents a new attribute-based learning algorithm, TS. Different from ID3, AQ11 and HCV in strategies, this algorithm operates in cycles of test and split. It uses those attribute values which occur only in positive examples but not in negative examples to discriminate positive examples against negative examples in a straight- forward manner and chooses the attributes with the least number of different values to split example sets. TS is natural, easy to implement, and polynomial in time complexity."</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Bionics and artificial intelligence</subfield><subfield code="2">sigle</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">Computer software</subfield><subfield code="2">sigle</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Machine learning</subfield></datafield><datafield tag="810" ind1="2" ind2=" "><subfield code="a">Department of Artificial Intelligence: DAI research paper</subfield><subfield code="t">University <Edinburgh></subfield><subfield code="v">568</subfield><subfield code="w">(DE-604)BV010450646</subfield><subfield code="9">568</subfield></datafield><datafield tag="943" ind1="1" ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-006968176</subfield></datafield></record></collection> |
| id | DE-604.BV010459098 |
| illustrated | Not Illustrated |
| indexdate | 2024-10-15T10:07:52Z |
| institution | BVB |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-006968176 |
| oclc_num | 1071638627 |
| open_access_boolean | |
| owner | DE-91G DE-BY-TUM |
| owner_facet | DE-91G DE-BY-TUM |
| physical | 13 S. |
| publishDate | 1991 |
| publishDateSearch | 1991 |
| publishDateSort | 1991 |
| record_format | marc |
| series2 | University <Edinburgh> / Department of Artificial Intelligence: DAI research paper |
| spelling | Wu, Xindong Verfasser aut TS, a test-split algorithm for inductive learning Edinburgh 1991 13 S. txt rdacontent n rdamedia nc rdacarrier University <Edinburgh> / Department of Artificial Intelligence: DAI research paper 568 Abstract: "This paper presents a new attribute-based learning algorithm, TS. Different from ID3, AQ11 and HCV in strategies, this algorithm operates in cycles of test and split. It uses those attribute values which occur only in positive examples but not in negative examples to discriminate positive examples against negative examples in a straight- forward manner and chooses the attributes with the least number of different values to split example sets. TS is natural, easy to implement, and polynomial in time complexity." Bionics and artificial intelligence sigle Computer software sigle Machine learning Department of Artificial Intelligence: DAI research paper University <Edinburgh> 568 (DE-604)BV010450646 568 |
| spellingShingle | Wu, Xindong TS, a test-split algorithm for inductive learning Bionics and artificial intelligence sigle Computer software sigle Machine learning |
| title | TS, a test-split algorithm for inductive learning |
| title_auth | TS, a test-split algorithm for inductive learning |
| title_exact_search | TS, a test-split algorithm for inductive learning |
| title_full | TS, a test-split algorithm for inductive learning |
| title_fullStr | TS, a test-split algorithm for inductive learning |
| title_full_unstemmed | TS, a test-split algorithm for inductive learning |
| title_short | TS, a test-split algorithm for inductive learning |
| title_sort | ts a test split algorithm for inductive learning |
| topic | Bionics and artificial intelligence sigle Computer software sigle Machine learning |
| topic_facet | Bionics and artificial intelligence Computer software Machine learning |
| volume_link | (DE-604)BV010450646 |
| work_keys_str_mv | AT wuxindong tsatestsplitalgorithmforinductivelearning |