Special Issue on Metalearning and Algorithm Selection

Guest Editors: Pavel Brazdil, Christophe Giraud-Carrier

Journal

Machine Learning

Machine Learning is an international forum focusing on computational approaches to learning.

Pavel Brazdil

-
Christophe Giraud-Carrier

-

Showing 1-10 of 10 articles

Metalearning and Algorithm Selection: progress, state of the art and introduction to the 2018 Special Issue
Pavel Brazdil
Christophe Giraud-Carrier
Editorial 29 December 2017 Pages: 1 - 14
Instance spaces for machine learning classification
Mario A. Muñoz
Laura Villanova
Kate Smith-Miles
OriginalPaper 28 December 2017 Pages: 109 - 147
Efficient benchmarking of algorithm configurators via model-based surrogates
Katharina Eggensperger
Marius Lindauer
Kevin Leyton-Brown
OriginalPaper 22 December 2017 Pages: 15 - 41
Scalable Gaussian process-based transfer surrogates for hyperparameter optimization
Martin Wistuba
Nicolas Schilling
Lars Schmidt-Thieme
OriginalPaper 22 December 2017 Pages: 43 - 78
Meta-QSAR: a large-scale application of meta-learning to drug design and discovery
Ivan Olier
Noureddin Sadawi
Ross D. King
OriginalPaper Open access 22 December 2017 Pages: 285 - 311
Data complexity meta-features for regression problems
Ana C. Lorena
Aron I. Maciel
Ricardo B. C. Prudêncio
OriginalPaper 21 December 2017 Pages: 209 - 246
Discovering predictive ensembles for transfer learning and meta-learning
Pavel Kordík
Jan Černý
Tomáš Frýda
OriginalPaper 21 December 2017 Pages: 177 - 207
The online performance estimation framework: heterogeneous ensemble learning for data streams
Jan N. van Rijn
Geoffrey Holmes
Joaquin Vanschoren
OriginalPaper Open access 21 December 2017 Pages: 149 - 176
Empirical hardness of finding optimal Bayesian network structures: algorithm selection and runtime prediction
Brandon Malone
Kustaa Kangas
Petri Myllymäki
OriginalPaper 20 December 2017 Pages: 247 - 283
Speeding up algorithm selection using average ranking and active testing by introducing runtime
Salisu Mamman Abdulrahman
Pavel Brazdil
Joaquin Vanschoren
OriginalPaper 14 November 2017 Pages: 79 - 108

Special Issue on Metalearning and Algorithm Selection

Participating journal

Machine Learning

Editors

Pavel Brazdil

Christophe Giraud-Carrier

Articles

Metalearning and Algorithm Selection: progress, state of the art and introduction to the 2018 Special Issue

Instance spaces for machine learning classification

Efficient benchmarking of algorithm configurators via model-based surrogates

Scalable Gaussian process-based transfer surrogates for hyperparameter optimization

Meta-QSAR: a large-scale application of meta-learning to drug design and discovery

Data complexity meta-features for regression problems

Discovering predictive ensembles for transfer learning and meta-learning

The online performance estimation framework: heterogeneous ensemble learning for data streams

Empirical hardness of finding optimal Bayesian network structures: algorithm selection and runtime prediction

Speeding up algorithm selection using average ranking and active testing by introducing runtime

Navigation

Special Issue on Metalearning and Algorithm Selection

Participating journal

Editors

Pavel Brazdil

Christophe Giraud-Carrier

Articles

Search

Navigation