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Projects / Programmes source: ARIS

Methods for developing hirarchical models

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Research activity

Code Science Field Subfield
2.07.07  Engineering sciences and technologies  Computer science and informatics  Intelligent systems - software 

Code Science Field
P176  Natural sciences and mathematics  Artificial intelligence 
Keywords
machine learning, constructive induction, structured induction, hierarchical models, multi-attribute decision making, function decomposition
Evaluation (rules)
source: COBISS
Evaluation of bibliographic research performance indicators according to ARIS methodology
Citations Citations for bibliographic records in COBIB.SI that are linked to records in citation databases
source: WoS source: Scopus source: COBISS
Researchers (2)
no. Code Name and surname Research area Role Period No. of publications
1.  02749  PhD Marko Bohanec  Computer science and informatics  Head  1998 - 1999  639 
2.  12536  PhD Blaž Zupan  Computer science and informatics  Researcher  1997 - 1999  531 
Organisations (1)
no. Code Research organisation City Registration number No. of publications
1.  0106  Jožef Stefan Institute  Ljubljana  5051606000  90,742 
Abstract
The goal of this project is to develop and investigate original methods for an active support of designers in developing hierarchical classification and evaluation models. The approach is based on (1) automatic and supervised development of models from learning examples, and (2) dynamic transformations of hybrid qualitative and quantitative decision models. Expected contribution of the project is a set of integrated methods that support the model development process at different levels: from an active support in a manual development of models to automatic and/or automated development based on machine learning from databases.
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