apprSubsampleSVMParetoFronts: Performance of different approximative SVM solvers
In danielhorn/multicrit_result_test: Multi-objective selection of algorithm portfolios
Description
A dataset containing the Pareto Fronts of differen approximative SVM sovlers
with respect to the objectives accuary and training time. A priori a multi-
objective parameter tuning has been done for every solver, the resulting
Pareto fronts of 10 independent optimizations runs on 4 data sets are given.
In contrast to the dataset apprSVMParetoFronts here each solver was
allowed to use subsampling as addition approximation strategy. The
subsampling rate itself was a parameter of the multi-objective tuning.
Usage
1
Format
A data frame with 3109 rows and 5 variables:
- dataset
Pareto front on which datatset
- solver
Pareto front for which SVM solver
- repl
number of replication
- error
first performance measure - error = 1 - accuary
- execTime
second performance measure - the training time
danielhorn/multicrit_result_test documentation built on May 14, 2019, 4:05 p.m.
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Description
A dataset containing the Pareto Fronts of differen approximative SVM sovlers
with respect to the objectives accuary and training time. A priori a multi-
objective parameter tuning has been done for every solver, the resulting
Pareto fronts of 10 independent optimizations runs on 4 data sets are given.
In contrast to the dataset apprSVMParetoFronts here each solver was
allowed to use subsampling as addition approximation strategy. The
subsampling rate itself was a parameter of the multi-objective tuning.
Usage
1 |
Format
A data frame with 3109 rows and 5 variables:
- dataset
Pareto front on which datatset
- solver
Pareto front for which SVM solver
- repl
number of replication
- error
first performance measure - error = 1 - accuary
- execTime
second performance measure - the training time
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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