| Name : R-frbs
| |
| Version : 3.2.0
| Vendor : obs://build_opensuse_org/devel:languages:R
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| Release : lp153.2.13
| Date : 2024-06-14 11:07:02
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| Group : Development/Libraries/Other
| Source RPM : R-frbs-3.2.0-lp153.2.13.src.rpm
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| Size : 1.52 MB
| |
| Packager : (none)
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| Summary : Fuzzy Rule-Based Systems for Classification and Regression Tasks
|
Description :
An implementation of various learning algorithms based on fuzzy
rule-based systems (FRBSs) for dealing with classification and
regression tasks. Moreover, it allows to construct an FRBS model
defined by human experts. FRBSs are based on the concept of fuzzy sets,
proposed by Zadeh in 1965, which aims at representing the reasoning of
human experts in a set of IF-THEN rules, to handle real-life problems
in, e.g., control, prediction and inference, data mining,
bioinformatics data processing, and robotics. FRBSs are also known as
fuzzy inference systems and fuzzy models. During the modeling of an
FRBS, there are two important steps that need to be conducted:
structure identification and parameter estimation. Nowadays, there
exists a wide variety of algorithms to generate fuzzy IF-THEN rules
automatically from numerical data, covering both steps. Approaches that
have been used in the past are, e.g., heuristic procedures, neuro-fuzzy
techniques, clustering methods, genetic algorithms, squares methods,
etc. Furthermore, in this version we provide a universal framework
named \'frbsPMML\', which is adopted from the Predictive Model Markup
Language (PMML), for representing FRBS models. PMML is an XML-based
language to provide a standard for describing models produced by data
mining and machine learning algorithms. Therefore, we are allowed to
export and import an FRBS model to/from \'frbsPMML\'. Finally, this
package aims to implement the most widely used standard procedures,
thus offering a standard package for FRBS modeling to the R community.
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RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
