Titlebook: Human and Machine Learning; Visible, Explainable Jianlong Zhou,Fang Chen Textbook 2018 Springer International Publishing AG, part of Spring

Callus

https://doi.org/10.1007/978-3-319-90403-0Machine Learning; Human Factors; Visualization; Explanation; Transparency

Pde5-Inhibitors

978-3-030-08007-5Springer International Publishing AG, part of Springer Nature 2018

旅行路線

Human and Machine Learning978-3-319-90403-0Series ISSN 1571-5035 Series E-ISSN 2524-4477

使害怕

Transparency Communication for Machine Learning in Human-Automation Interactiond agents using automated explanations. We will discuss the application of a particular ML method, reinforcement learning (RL), in Partially Observable Markov Decision Process (POMDP)-based agents, and the design of explanation algorithms for RL in POMDPs.

神圣將軍

Explaining the Predictions of an Arbitrary Prediction Model: Feature Contributions and Quasi-nomogras work on a general method for explaining arbitrary prediction models (classification or regression) to a general methodology for constructing a quasi-nomogram for a black-box prediction model. We show that for an additive model, such a quasi-nomogram is equivalent to the one we would construct if t

朦朧

爭(zhēng)議的蘋果

Discrete

Textbook 2018stematically understand the current active research activities and outcomes related to human and machine learning. The book will not only inspire researchers to passionately develop new algorithms incorporating human for human-centred ML algorithms, resulting in the overall advancement of ML, but al

faultfinder

1571-5035 an and machine learning. The book will not only inspire researchers to passionately develop new algorithms incorporating human for human-centred ML algorithms, resulting in the overall advancement of ML, but al978-3-030-08007-5978-3-319-90403-0Series ISSN 1571-5035 Series E-ISSN 2524-4477

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Mohammed Brahimi,Marko Arsenovic,Sohaib Laraba,Srdjan Sladojevic,Kamel Boukhalfa,Abdelouhab Moussaou