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Last data update: 2014.03.03

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NHEMOtree : Non-hierarchical evolutionary multi-objective tree learner to perform cost-sensitive classification

Package: NHEMOtree
Type: Package
Title: Non-hierarchical evolutionary multi-objective tree learner to
perform cost-sensitive classification
Depends: partykit, emoa, sets, rpart
Version: 1.0
Date: 2013-05-05
Author: Swaantje Casjens
Maintainer: Swaantje Casjens <swaantje.casjens@tu-dortmund.de>
Description: NHEMOtree performs cost-sensitive classification by
solving the two-objective optimization problem of minimizing
misclassification rate and minimizing total costs for
classification. The three methods comprised in NHEMOtree are
based on EMOAs with either tree representation or bitstring
representation with an enclosed classification tree algorithm.
License: GPL-3
Packaged: 2013-05-09 09:55:32 UTC; hendrik
NeedsCompilation: no
Repository: CRAN
Date/Publication: 2013-05-09 15:46:02

● Data Source: CranContrib
● 4 images, 7 functions, 0 datasets
● Reverse Depends: 0

GPareto : Gaussian Processes for Pareto Front Estimation and Optimization

Package: GPareto
Title: Gaussian Processes for Pareto Front Estimation and Optimization
Version: 1.0.2
Date: 2016-03-01
Author: Mickael Binois, Victor Picheny
Maintainer: Mickael Binois <mickael.binois@mines-stetienne.fr>
Description: Gaussian process regression models, a.k.a. kriging models, are
applied to global multiobjective optimization of black-box functions.
Multiobjective Expected Improvement and Step-wise Uncertainty Reduction
sequential infill criteria are available. A quantification of uncertainty
on Pareto fronts is provided using conditional simulations.
License: GPL-3
Depends: DiceKriging (>= 1.5.3), emoa, methods
Imports: Rcpp (>= 0.11.1), rgenoud, pbivnorm, pso, randtoolbox,
KrigInv, MASS, DiceDesign (>= 1.4), ks
Suggests: knitr
VignetteBuilder: knitr
LinkingTo: Rcpp
Repository: CRAN
RoxygenNote: 5.0.1
NeedsCompilation: yes
Packaged: 2016-03-01 15:31:54 UTC; vpicheny
Date/Publication: 2016-03-02 02:13:50

● Data Source: CranContrib
● 20 images, 22 functions, 0 datasets
● Reverse Depends: 0