Designing neural network architectures using reinforcement learning, 2016. ,
Bilevel optimization and machine learning, Computational Intelligence: Research Frontiers, pp.25-47, 2008. ,
Compression-based averaging of selective naive bayes classifiers, Journal of Machine Learning Research, vol.8, pp.1659-1685, 2007. ,
A parameter-free classification method for large scale learning, Journal of Machine Learning Research, vol.10, pp.1367-1385, 2009. ,
Ensemble selection from libraries of models, Twenty-first international conference on Machine learning , ICML '04, p.18, 2004. ,
DOI : 10.1145/1015330.1015432
URL : http://www.aicml.cs.ualberta.ca/banff04/icml/pages/papers/345.ps
, , 2001.
Towards an empirical foundation for assessing Bayesian optimization of hyperparameters, NIPS workshop on Bayesian Optimization in Theory and Practice, 2013. ,
Particle swarm model selection, Journal of Machine Learning Research, vol.10, pp.405-440, 2009. ,
Efficient and robust automated machine learning, Proceedings of the Neural Information Processing Systems, pp.2962-2970, 2015. ,
Methods for improving bayesian optimization for automl, Proceedings of the International Conference on Machine Learning 2015, Workshop on Automatic Machine Learning, 2015. ,
Combining hyperband and bayesian optimization, BayesOpt 2017 NIPS Workshop on Bayesian Optimization, 2017. ,
Practical hyperparameter optimization, International Conference on Learning Representations 2018 Workshop track, 2018. ,
Initializing bayesian hyperparameter optimization via meta-learning, Proceedings of the AAAI Conference on Artificial Intelligence, pp.1128-1135, 2015. ,
Neural Networks and the Bias/Variance Dilemma, Neural Computation, vol.36, issue.1, pp.1-58, 1992. ,
DOI : 10.1162/neco.1990.2.1.1
Analysis of the AutoML challenge series 2015-2018, Automatic Machine Learning. Springer series in Challanges in Machine Learning, 2017. ,
, Feature extraction, foundations and applications . Studies in Fuzziness and Soft Computing, 2006.
The WEKA data mining software, ACM SIGKDD Explorations Newsletter, vol.11, issue.1, pp.10-18, 2009. ,
DOI : 10.1145/1656274.1656278
, Sequential Model-based Algorithm Configuration (SMAC)
The elements of statistical learning: Data mining, inference, and prediction, 2001. ,
, , pp.2018-2024
Freeze Thaw Ensemble Construction Logitboost with trees applied to the WCCI 2006 performance prediction challenge datasets, Proc. IJCNN06, pp.2966-2969, 2006. ,
Model selection for primal SVM, Machine Learning, 2011. ,
DOI : 10.1145/1281192.1281270
URL : https://link.springer.com/content/pdf/10.1007%2Fs10994-011-5246-7.pdf
Scikit-learn: Machine learning in Python, Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011. ,
A survey on transfer learning, IEEE Transactions on Knoweledge and Data Engineering, vol.22, issue.10, pp.1345-1359, 2010. ,
Modeling by shortest data description, Automatica, vol.14, issue.5, pp.465-471, 1978. ,
Full model selection in the space of data mining operators, Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion, GECCO Companion '12, pp.1503-1504, 2012. ,
DOI : 10.1145/2330784.2331014
URL : https://researchcommons.waikato.ac.nz/bitstream/10289/6339/1/Sun%20Pfahringer%20Mayo%202012.pdf
Learning with Kernels: Support Vector Machines, Regularization, Optimization , and Beyond, 2001. ,
Autoweka: Automated selection and hyperparameter optimization of classification algorithms, 1208. ,
Statistical learning theory, 1998. ,
OpenML, ACM SIGKDD Explorations Newsletter, vol.15, issue.2, pp.49-60, 2013. ,
DOI : 10.1145/2641190.2641198
URL : http://arxiv.org/pdf/1407.7722
Neural architecture search with reinforcement learning. arXiv preprint, 2016. ,