Titlebook: Artificial Intelligence XXXVI; 39th SGAI Internatio Max Bramer,Miltos Petridis Conference proceedings 2019 Springer Nature Switzerland AG 2

發(fā)酵劑

Chandelier

https://doi.org/10.1007/978-3-030-59908-9wo scenarios: enforcing and not enforcing the constraints. The results show that enforcement of monotonicity constraints can consistently improve the predictive accuracy of the constructed models. The produced models are fully monotonic according to the monotonicity constraints, which can have a pos

無孔

https://doi.org/10.1007/978-3-030-53571-1vide the design and implementation details of our new tool along with an evaluation of the software. The tool we have produced captures the distinctive features of each of the two dialogue types, to make plain their differences and to validate the speech acts for use in practical scenarios.

Obedient

CLOWN

https://doi.org/10.1007/978-3-031-66395-6efine a custom prioritization strategy, to achieve the best possible result. This paper describes a novel workflow architecture for heavy knowledge-related application workflows to address the tasks of high solution accuracy and shorter prediction resolution time. We describe how policies can be gen

PLAYS

https://doi.org/10.1007/978-3-031-66395-6 To the best of our knowledge no similar dataset, that captures weapon-based violence, exists. The paper evaluates the proposed dataset by utilising local feature descriptors using an SVM classifier. The extracted features are aggregated using the Bag of Visual Word (BoVW) technique to classify weap

敬禮

Artificial Intelligence XXXVI978-3-030-34885-4Series ISSN 0302-9743 Series E-ISSN 1611-3349

Vulnerary

https://doi.org/10.1007/978-3-319-00681-9sification is to train individual binary classifiers per label, but the performance can be improved by considering associations between the labels, and algorithms like classifier chains and RAKEL do this effectively. Like most machine learning algorithms, however, these approaches require accurate h

Trypsin

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