Pruning Hypothesis Spaces Using Learned Domain Theories
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00320168" target="_blank" >RIV/68407700:21230/18:00320168 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-3-319-78090-0_11" target="_blank" >http://dx.doi.org/10.1007/978-3-319-78090-0_11</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-319-78090-0_11" target="_blank" >10.1007/978-3-319-78090-0_11</a>
Alternative languages
Result language
angličtina
Original language name
Pruning Hypothesis Spaces Using Learned Domain Theories
Original language description
We present a method to prune hypothesis spaces in the con- text of inductive logic programming. The main strategy of our method consists in removing hypotheses that are equivalent to already consid- ered hypotheses. The distinguishing feature of our method is that we use learned domain theories to check for equivalence, in contrast to existing approaches which only prune isomorphic hypotheses. Specifically, we use such learned domain theories to saturate hypotheses and then check if these saturations are isomorphic. While conceptually simple, we exper- imentally show that the resulting pruning strategy can be surprisingly effective in reducing both computation time and memory consumption when searching for long clauses, compared to approaches that only con- sider isomorphism.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA17-26999S" target="_blank" >GA17-26999S: Deep relational learning</a><br>
Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Inductive Logic Programming 2017
ISBN
978-3-319-78089-4
ISSN
0302-9743
e-ISSN
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Number of pages
17
Pages from-to
152-168
Publisher name
Springer International Publishing
Place of publication
Cham
Event location
Orléans
Event date
Sep 4, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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