| nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
| 1 |
Data complexity meta-features for regression problems
|
Lorena, Ana C.
|
|
2017
|
107 |
1 |
p. 209-246
|
artikel
|
| 2 |
Discovering predictive ensembles for transfer learning and meta-learning
|
Kordík, Pavel
|
|
2017
|
107 |
1 |
p. 177-207
|
artikel
|
| 3 |
Efficient benchmarking of algorithm configurators via model-based surrogates
|
Eggensperger, Katharina
|
|
2017
|
107 |
1 |
p. 15-41
|
artikel
|
| 4 |
Empirical hardness of finding optimal Bayesian network structures: algorithm selection and runtime prediction
|
Malone, Brandon
|
|
2017
|
107 |
1 |
p. 247-283
|
artikel
|
| 5 |
Instance spaces for machine learning classification
|
Muñoz, Mario A.
|
|
2017
|
107 |
1 |
p. 109-147
|
artikel
|
| 6 |
Metalearning and Algorithm Selection: progress, state of the art and introduction to the 2018 Special Issue
|
Brazdil, Pavel
|
|
2017
|
107 |
1 |
p. 1-14
|
artikel
|
| 7 |
Meta-QSAR: a large-scale application of meta-learning to drug design and discovery
|
Olier, Ivan
|
|
2017
|
107 |
1 |
p. 285-311
|
artikel
|
| 8 |
Scalable Gaussian process-based transfer surrogates for hyperparameter optimization
|
Wistuba, Martin
|
|
2017
|
107 |
1 |
p. 43-78
|
artikel
|
| 9 |
Speeding up algorithm selection using average ranking and active testing by introducing runtime
|
Abdulrahman, Salisu Mamman
|
|
2017
|
107 |
1 |
p. 79-108
|
artikel
|
| 10 |
The online performance estimation framework: heterogeneous ensemble learning for data streams
|
Rijn, Jan N. van
|
|
2017
|
107 |
1 |
p. 149-176
|
artikel
|
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