Learning and Model-checking Networks of I/O Automata

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2 Citationer (Scopus)

Abstrakt

We introduce a new statistical relational learning (SRL) approach in which models for
structured data, especially network data, are constructed as networks of communicating
nite probabilistic automata. Leveraging existing automata learning methods from the area
of grammatical inference, we can learn generic models for network entities in the form of
automata templates. As is characteristic for SRL techniques, the abstraction level aorded
by learning generic templates enables one to apply the learned model to new domains. A
main benet of learning models based on nite automata lies in the fact that one can analyse
the resulting models using formal model-checking techniques, which adds a dimension of
model analysis not usually available for traditional SRL modeling frameworks.
OriginalsprogEngelsk
TitelProceeding of the 4th Asian Conference on Machine Learning (ACML 2012)
RedaktørerSteven C. H. Hoi, Wray Buntine
Publikationsdato2012
Sider285-300
StatusUdgivet - 2012
BegivenhedAsian Conference on Machine Learning - Singapore, Singapore
Varighed: 4 nov. 20126 nov. 2012
Konferencens nummer: 4

Konference

KonferenceAsian Conference on Machine Learning
Nummer4
LandSingapore
BySingapore
Periode04/11/201206/11/2012
NavnJMLR Workshop and Conference Proceedings
Vol/bind25
ISSN1938-7228

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