Editorial: Machine learning for safety–critical applications in engineering.
- Published in:
- Machine Learning, 2020, v. 109, n. 5, p. 1101, doi. 10.1007/s10994-020-05876-6
- By:
- Publication type:
- Article
Works matching IS 08856125 AND DT 2020 AND VI 109 AND IP 5
Results: 8
1
2
Engineering problems in machine learning systems.
- Published in:
- Machine Learning, 2020, v. 109, n. 5, p. 1103, doi. 10.1007/s10994-020-05872-w
- By:
- Publication type:
- Article
3
Joint maximization of accuracy and information for learning the structure of a Bayesian network classifier.
- Published in:
- Machine Learning, 2020, v. 109, n. 5, p. 1039, doi. 10.1007/s10994-020-05869-5
- By:
- Publication type:
- Article
4
Detecting anomalous packets in network transfers: investigations using PCA, autoencoder and isolation forest in TCP.
- Published in:
- Machine Learning, 2020, v. 109, n. 5, p. 1127, doi. 10.1007/s10994-020-05870-y
- By:
- Publication type:
- Article
5
Sparse hierarchical regression with polynomials.
- Published in:
- Machine Learning, 2020, v. 109, n. 5, p. 973, doi. 10.1007/s10994-020-05868-6
- By:
- Publication type:
- Article
6
Improved graph-based SFA: information preservation complements the slowness principle.
- Published in:
- Machine Learning, 2020, v. 109, n. 5, p. 999, doi. 10.1007/s10994-019-05860-9
- By:
- Publication type:
- Article
7
Model-based kernel sum rule: kernel Bayesian inference with probabilistic models.
- Published in:
- Machine Learning, 2020, v. 109, n. 5, p. 939, doi. 10.1007/s10994-019-05852-9
- By:
- Publication type:
- Article
8
High-dimensional model recovery from random sketched data by exploring intrinsic sparsity.
- Published in:
- Machine Learning, 2020, v. 109, n. 5, p. 899, doi. 10.1007/s10994-019-05865-4
- By:
- Publication type:
- Article