Titlebook: Explainable and Interpretable Models in Computer Vision and Machine Learning; Hugo Jair Escalante,Sergio Escalera,Marcel‘van Ger Book 2018

推測

Ahu Yazici Ayyildiz,Erdogan Koc provide easy-to-interpret rationales for their behavior—so that passengers, insurance companies, law enforcement, developers etc., can understand what triggered a particular behavior. Here, we explore the use of visual explanations. These explanations take the form of real-time highlighted regions

弓箭

forecast

Miranda Giacomin,Christian H. Jordanticular, assessment of apparent personality gives insights into the first impressions evoked by a candidate. Such analysis tools can be used for training purposes, if they can be configured to provide appropriate and clear feedback. In this chapter, we describe a multimodal system that analyzes a sh

Surgeon

Essential Cerebrovascular Hemodynamics,man-machine interactions. Expressing the rationale behind such a system’s output is an important aspect of human-machine interaction as AI continues to be prominent in general, everyday use-cases. In this paper, we introduce a novel framework integrating Grenander’s pattern theory structures to prod

obstinate

Springer Nature Switzerland AG 2018

wangle

Explainable and Interpretable Models in Computer Vision and Machine Learning978-3-319-98131-4Series ISSN 2520-131X Series E-ISSN 2520-1328

追蹤

Analogy

Learning Interpretable Rules for Multi-Label Classificationel data. Discussing this task in detail, we highlight some of the problems that make rule learning considerably more challenging for MLC than for conventional classification. While mainly focusing on our own previous work, we also provide a short overview of related work in this area.

反感

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