Titlebook: Machine Learning and Knowledge Discovery in Databases; International Worksh Peggy Cellier,Kurt Driessens Conference proceedings 2020 Spring

艱苦地移動(dòng)

Effect of Superpixel Aggregation on Explanations in LIME – A Case Study with Biological Dataevance areas with the image parts marked by a human reference. Results show that image parts selected as relevant strongly vary depending on the applied method. Quick-Shift resulted in the least and Compact-Watershed in the highest correspondence with the reference relevance areas.

EXCEL

pus840

Quantifying Model Complexity via Functional Decomposition for Better Post-hoc Interpretability models that minimize the three measures is more reliable and compact. Furthermore, we demonstrate the application of these measures in a multi-objective optimization approach which simultaneously minimizes loss and complexity.

Infirm

Coronation

Tortuous

Vasodilation

咽下

ReinBo: Machine Learning Pipeline Conditional Hierarchy Search and Configuration with Bayesian Optim, we propose an efficient pipeline search and configuration algorithm which combines the power of Reinforcement Learning and Bayesian Optimization. Empirical results show that our method performs favorably compared to state of the art methods like Auto-sklearn, TPOT, Tree Parzen Window, and Random Search.

heterodox

SynthLog: A Language for Synthesising Inductive Data Models (Extended Abstract)reasoning. It is used as the back-end of the automated data scientist approach that is being developed in the SYNTH project. An overview of the SynthLog philosophy and language as well as a non trivial example of its use, is given in this paper.

跳脫衣舞的人

The ABC of Data: A Classifying Framework for Data Readiness proposed to fit this need, but they require a more detailed and measurable definition than is given in prior works. We present a practical framework focused on machine learning that encapsulates data cleaning and (pre)processing procedures. In our framework, datasets are classified within bands, an