Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Annalisa Appice,Pedro Pereira Rodrigues,Alípio Jor Conference p

abduction

放逐某人

Fast Label Embeddings via Randomized Linear Algebrasettings. The result is a randomized algorithm whose running time is exponentially faster than naive algorithms. We demonstrate our techniques on two large-scale public datasets, from the Large Scale Hierarchical Text Challenge and the Open Directory Project, where we obtain state of the art results.

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Regression with Linear Factored FunctionsWe derive a regularized greedy optimization scheme, that learns factored basis functions during training. The novel regression algorithm performs competitively to . on benchmark tasks, and the learned LFF functions are with 4-9 factored basis functions on average very compact.

Apoptosis

Ridge Regression, Hubness, and Zero-Shot Learningprove that the proposed approach indeed reduces hubness. This was verified empirically on the tasks of bilingual lexicon extraction and image labeling: hubness was reduced with both of these tasks and the accuracy was improved accordingly.

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Erythropoietin

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Customary

ConDist: A Context-Driven Categorical Distance Measureal distance measures and evaluate on different data sets from the UCI machine-learning repository. The experiments show that our distance measure is recommendable, since it achieves similar or better results in a more robust way than previous approaches.

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Conference proceedings 2015overy in Databases, ECML PKDD 2015, held in Porto, Portugal, in September 2015. .The 131 papers presented in these proceedings were carefully reviewed and selected from a total of 483 submissions. These include 89 research papers, 11 industrial papers, 14 nectar papers, and 17 demo papers. They were