Výsledky vyhledávání - "Erick Alphonse"

On the connection between the phase transition of the covering test and the learning success rate in ILP

Publikováno v: ILP

It is well-known that heuristic search in ILP is prone to plateau phenomena. An explanation can be given after the work of Giordana and Saitta: the ILP covering test is NP-complete and therefore exhibits a sharp phase transition in its coverage proba

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::e2ce53ad9c2eadc170c3768945c64b96
https://doi.org/10.1007/s10994-007-5031-9

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[Untitled]

Autor: Stan Matwin, Erick Alphonse

Publikováno v: Journal of Intelligent Information Systems. 22:23-40

Attribute-value based representations, standard in today's data mining systems, have a limited expressiveness. Inductive Logic Programming provides an interesting alternative, particularly for learning from structured examples whose parts, each with

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::085cb55405e3d1792ad7ea0580da6ec9
https://doi.org/10.1023/a:1025876613117

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BioNLP Shared Task--The Bacteria Track

Autor: Julien Jourde, Philippe Veber, Erick Alphonse, Alain-Pierre Manine, Philippe Bessières, Claire Nédellec, Maarten van de Guchte, Robert Bossy

Publikováno v: BMC Bioinformatics
BMC Bioinformatics (13), . (2012)
BMC Bioinformatics, BioMed Central, 2012, 13, ⟨10.1186/1471-2105-13-S11-S3⟩
Scopus-Elsevier

Background We present the BioNLP 2011 Shared Task Bacteria Track, the first Information Extraction challenge entirely dedicated to bacteria. It includes three tasks that cover different levels of biological knowledge. The Bacteria Gene Renaming suppo

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c78b7911fded0f82ee052537a58a9b1a
https://pubmed.ncbi.nlm.nih.gov/22759457

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A Numerical Refinement Operator Based on Multi-Instance Learning

Autor: Erick Alphonse, Tobias Girschick, Fabian Buchwald, Stefan Kramer

Publikováno v: Inductive Logic Programming ISBN: 9783642212949
ILP

We present a numerical refinement operator based on multiinstance learning. In the approach, the task of handling numerical variables in a clause is delegated to statistical multi-instance learning schemes. To each clause, there is an associated mult

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::72141abe1d956775961f5b292a5d6eca
https://doi.org/10.1007/978-3-642-21295-6_5

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Extraction of genic interactions with the recursive logical theory of an ontology

Autor: Philippe Bessières, Alain-Pierre Manine, Erick Alphonse

Publikováno v: 11. International conference on intelligent text processing and computational linguistics (CICLing'10)
11. International conference on intelligent text processing and computational linguistics (CICLing'10), 2010, Iasi, Romania
Computational linguistics and intelligent text processing: 11th international conference, CICLing 2010, Iasi, Romania, March 21-27, 2010, proceedings. 2010; 11. International conference on intelligent text processing and computational linguistics (CICLing'10), Iasi, ROU, 2010-, 549-563
Computational Linguistics and Intelligent Text Processing ISBN: 9783642121159
CICLing

International audience; We introduce an Information Extraction (IE) system which uses the logical theory of an ontology as a generalisation of the typical information extraction patterns to extract biological interactions from text. This provides inf

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8f665a327af4061006d5438ae7c81053
https://hal.inrae.fr/hal-02758320/document

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Empirical Study of Relational Learning Algorithms in the Phase Transition Framework

Autor: Aomar Osmani, Erick Alphonse

Publikováno v: Machine Learning and Knowledge Discovery in Databases ISBN: 9783642041792
ECML/PKDD (1)

Relational Learning (RL) has aroused interest to fill the gap between efficient attribute-value learners and growing applications stored in multi-relational databases. However, current systems use general- purpose problem solvers that do not scale-up

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::008f0b4138c8f07f16b6a909cc6a6e72
https://doi.org/10.1007/978-3-642-04180-8_21

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Information extraction as an ontology population task and Its application to genic interactions

Autor: Erick Alphonse, A.-P. Manine, Philippe Bessières

Publikováno v: Proceedings-International Conference on Tools with Artificial Intelligence, TAI
20. IEEE International Conference on Tools with Artificial Intelligence
20. IEEE International Conference on Tools with Artificial Intelligence, Nov 2008, Dayton, United States. ⟨10.1109/ICTAI.2008.117⟩
ICTAI (2)

International audience; Ontologies are a well-motivated formal representation to model knowledge needed to extract and encode data from text. Yet, their tight integration with Information Extraction (IE) systems is still a research issue, a fortiori

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fd2acac40ab33362bb028fed22d7fb68
https://hal.inrae.fr/hal-02756569

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Learning ontological rules to extract multiple relations of genic interactions from text

Autor: Alain-Pierre Manine, Erick Alphonse, Philippe Bessières

Publikováno v: International Journal of Medical Informatics
International Journal of Medical Informatics, Elsevier, 2009, 78 (12), pp.E31-E38. ⟨10.1016/j.ijmedinf.2009.03.005⟩

International audience; Introduction: Information extraction (IE) systems have been proposed in recent years to extract genic interactions from bibliographical resources. They are limited to single interaction relations, and have to face a trade-off

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4bacd3dc39d540d95e818d2d58b0ae79
https://pubmed.ncbi.nlm.nih.gov/19398370

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A Model to Study Phase Transition and Plateaus in Relational Learning

Autor: Erick Alphonse, Aomar Osmani

Publikováno v: Inductive Logic Programming ISBN: 9783540859277
ILP

The feasibility of symbolic learning strongly relies on the efficiency of heuristic search in the hypothesis space. However, recent works in relational learning claimed that the phase transition phenomenon which may occur in the subsumption test duri

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::fb371e64d23c29e3ea054e9fa7b1ce20
https://doi.org/10.1007/978-3-540-85928-4_6

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Phase transition and heuristic search in relational learning

Autor: Erick Alphonse, Aomar Osmani

Publikováno v: ICMLA

We introduce a supervised reinforcement learning (SRL) architecture for robot control problems with high dimensional state spaces. Based on such architecture two new SRL algorithms are proposed. In our algorithms, a behavior model learned from exampl

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::014ebbcd0ca8beecab013b98c769cf1d
https://doi.org/10.1109/icmla.2007.102

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