Titlebook: Machine Learning and Knowledge Extraction; Third IFIP TC 5, TC Andreas Holzinger,Peter Kieseberg,Edgar Weippl Conference proceedings 2019

BET

Machine Learning and Knowledge Extraction978-3-030-29726-8Series ISSN 0302-9743 Series E-ISSN 1611-3349

裂隙

CRACY

KANDINSKY Patterns as IQ-Test for Machine Learning,idation and training of explainability in AI. Kandinsky Patterns are at the same time easily distinguishable from human observers. Consequently, controlled patterns can be described by . humans and computers. The results of our study show that the majority of human explanations was made based on the

Initiative

PACK

Estimating the Driver Status Using Long Short Term Memory,ituations and warn the drivers. We conducted an experiment in eight different driving scenarios and collected a large dataset from driving data and driver related data. We chose Long Short Term Memory (LSTM) as our machine learning method. We built and trained a stacked LSTM network to estimate the

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Using Relational Concept Networks for Explainable Decision Support, steer the interpretation of the little data available with the knowledge of the expert..We created a framework that consists of concepts and relations between those concepts, for which the exact relational importance is not necessarily specified. A machine-learning approach is used to determine the

神經(jīng)

破裂

Hot-Flash

Ranked MSD: A New Feature Ranking and Feature Selection Approach for Biomarker Identification,, we have developed a new algorithm for feature ranking and feature selection, named .. We have tested our proposed approach on two real-world gene expression data sets, both of which relate to respiratory viral infections. This Ranked MSD feature selection algorithm is able to reduce the feature se

chisel

How to Improve the Adaptation Phase of the CBR in the Medical Domain,s very different from the stored ones, so the input from participated experts is needed, and a new rule will be stored. (3) The new case is partially similar satisfying adaptability conditions, in such a situation we adopt related decision rule to the new conditions under the supervision of the expe