Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Michelangelo Ceci,Jaakko Hollmén,Sašo Džeroski Conference proce

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Efficient Sequence Regression by Learning Linear Models in All-Subsequence Spacelting linear regression model provides the user with a list of the most predictive features selected during the learning stage, adding to the interpretability of the method. Code and data related to this chapter are available at: ..

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Bayesian Inference for Least Squares Temporal Difference Regularizationilistic predictions, a sparse model, and good generalisation capabilities, as irrelevant parameters are marginalised out. The algorithm efficiently approximates the posterior distribution through variational inference. We demonstrate the ability of the algorithm in avoiding overfitting experimentally.

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PAC-Bayesian Analysis for a Two-Step Hierarchical Multiview Learning Approach of the view-specific classifiers. From this result it comes out that controlling the trade-off between diversity and accuracy is a key element for multiview learning, which complements other results in multiview learning. Finally, we experiment our principle on multiview datasets extracted from the Reuters RCV1/RCV2 collection.

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0302-9743 rence on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2017, held in Skopje, Macedonia, in September 2017. .The total of 101 regular papers presented in part I and part II was carefully reviewed and selected from 364 submissions; there are 47 papers in the applied data science, ne

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Multi-view Generative Adversarial Networksodel, the Multi-view BiGAN (MV-BiGAN) is able to perform density estimation from multi-view inputs. Second, it can deal with missing views and is able to update its prediction when additional views are provided. We illustrate these properties on a set of experiments over different datasets.

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978-3-319-71245-1Springer International Publishing AG 2017

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Machine Learning and Knowledge Discovery in Databases978-3-319-71246-8Series ISSN 0302-9743 Series E-ISSN 1611-3349