Titlebook: Ensembles in Machine Learning Applications; Oleg Okun,Giorgio Valentini,Matteo Re Book 2011 Springer Berlin Heidelberg 2011 Computational

天空

細(xì)節(jié)

Bias-Variance Analysis of ECOC and Bagging Using Neural Nets,o understand the overall trends when the parameters of the base classifiers – nodes and epochs for NNs –, are changed. We show experimentally on 5 artificial and 4 UCI MLR datasets that there are some clear trends in the analysis that should be taken into consideration while designing NN classifier systems.

演講

Fast-Ensembles of Minimum Redundancy Feature Selection,me prevents them from scaling up to real-world applications.We propose two methods which enhance correlation-based feature selection such that the stability of feature selection comes with little or even no extra runtime.We show the efficiency of the algorithms analytically and empirically on a wide range of datasets.

暫停,間歇

Learning Markov Blankets for Continuous or Discrete Networks via Feature Selection,nce for feature selection criteria. We compare our performance in the causal structure learning problem to a collection of common feature selection methods.We also compare to Bayesian local structure learning. These results can also be easily extended to other casual structure models such as undirected graphical models.

Bravura

ADAGE

音的強(qiáng)弱

Obsessed

aerial

https://doi.org/10.1007/978-981-19-5106-0several ensemble methods: Bagging , Random Subspaces, AdaBoost.R2 and Iterated Bagging. For all the considered methods and variants, ensembles with Random Oracles are better than the corresponding version without the Oracles.

調(diào)整