Attributes | Values |
---|
rdf:type
| |
Description
| - Vyvinuli jsme algoritmus, který odhaduje pokrytí klauzule, tj. počet příkladů, které jsou klauzulí implikovány. Algoritmus je implementován a testován na vygenerovaných i realných datových sadách. (cs)
- In relational learning, {$\theta$}-subsumption and OI-subsumption are widely used coverage tests. Unfortunately, testing both {$\theta$}-subsumption and OI-subsumption is NP-complete, which is one of the reasons for poor performance of most relational learners. In this paper, we aim at a possible exploitation of randomized restarted search strategies for coverage estimation. We devise an algorithm that can, under a distributional assumption, estimate coverage between a given hypothesis and a set of examples without having to decide the subsumption test for all examples. We implement this algorithm in programs {\sc ReCovEr} and {\sc ReCovErOI}. On artificial graph data and real-world datasets, we show that these algorithms can provide reasonably accurate estimates while achieving favorable runtimes as compared to state-of-the-art algorithms.
- In relational learning, {$\theta$}-subsumption and OI-subsumption are widely used coverage tests. Unfortunately, testing both {$\theta$}-subsumption and OI-subsumption is NP-complete, which is one of the reasons for poor performance of most relational learners. In this paper, we aim at a possible exploitation of randomized restarted search strategies for coverage estimation. We devise an algorithm that can, under a distributional assumption, estimate coverage between a given hypothesis and a set of examples without having to decide the subsumption test for all examples. We implement this algorithm in programs {\sc ReCovEr} and {\sc ReCovErOI}. On artificial graph data and real-world datasets, we show that these algorithms can provide reasonably accurate estimates while achieving favorable runtimes as compared to state-of-the-art algorithms. (en)
|
Title
| - Fast Estimation of First Order Clause Coverage through Randomization and Maximum Likelihood
- Fast Estimation of First Order Clause Coverage through Randomization and Maximum Likelihood (en)
- Rychlý odhad pokrytí klauzule prostřednictvím randomizace a maximální věrohodnosti (cs)
|
skos:prefLabel
| - Fast Estimation of First Order Clause Coverage through Randomization and Maximum Likelihood
- Fast Estimation of First Order Clause Coverage through Randomization and Maximum Likelihood (en)
- Rychlý odhad pokrytí klauzule prostřednictvím randomizace a maximální věrohodnosti (cs)
|
skos:notation
| - RIV/68407700:21230/08:03145218!RIV09-GA0-21230___
|
http://linked.open...avai/riv/aktivita
| |
http://linked.open...avai/riv/aktivity
| |
http://linked.open...vai/riv/dodaniDat
| |
http://linked.open...aciTvurceVysledku
| |
http://linked.open.../riv/druhVysledku
| |
http://linked.open...iv/duvernostUdaju
| |
http://linked.open...titaPredkladatele
| |
http://linked.open...dnocenehoVysledku
| |
http://linked.open...ai/riv/idVysledku
| - RIV/68407700:21230/08:03145218
|
http://linked.open...riv/jazykVysledku
| |
http://linked.open.../riv/klicovaSlova
| - clause coverage; randomization; relational learning (en)
|
http://linked.open.../riv/klicoveSlovo
| |
http://linked.open...ontrolniKodProRIV
| |
http://linked.open...v/mistoKonaniAkce
| |
http://linked.open...i/riv/mistoVydani
| |
http://linked.open...i/riv/nazevZdroje
| - Proceedings of the 25th International Conference on Machine Learning
|
http://linked.open...in/vavai/riv/obor
| |
http://linked.open...ichTvurcuVysledku
| |
http://linked.open...cetTvurcuVysledku
| |
http://linked.open...vavai/riv/projekt
| |
http://linked.open...UplatneniVysledku
| |
http://linked.open...iv/tvurceVysledku
| - Kuželka, Ondřej
- Železný, Filip
|
http://linked.open...vavai/riv/typAkce
| |
http://linked.open.../riv/zahajeniAkce
| |
number of pages
| |
http://purl.org/ne...btex#hasPublisher
| |
https://schema.org/isbn
| |
http://localhost/t...ganizacniJednotka
| |
is http://linked.open...avai/riv/vysledek
of | |