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

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書目名稱Explainable and Interpretable Models in Computer Vision and Machine Learning影響因子(影響力)




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書目名稱Explainable and Interpretable Models in Computer Vision and Machine Learning網(wǎng)絡公開度學科排名




書目名稱Explainable and Interpretable Models in Computer Vision and Machine Learning被引頻次




書目名稱Explainable and Interpretable Models in Computer Vision and Machine Learning被引頻次學科排名




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書目名稱Explainable and Interpretable Models in Computer Vision and Machine Learning讀者反饋




書目名稱Explainable and Interpretable Models in Computer Vision and Machine Learning讀者反饋學科排名

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Bruno Allevato,Suzana Kahn Ribeirofor their outputs. These explanations are often used to qualitatively assess other criteria such as safety or non-discrimination. However, despite the interest in interpretability, there is little consensus on what interpretable machine learning is and how it should be measured and evaluated. In thi

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https://doi.org/10.1007/978-3-031-18448-2ted with several class labels simultaneously. In this chapter, we advocate a rule-based approach to multi-label classification. Rule learning algorithms are often employed when one is not only interested in accurate predictions, but also requires an interpretable theory that can be understood, analy

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Handbook of Top Management Teamssimpler models are more interpretable than more complex models with higher performance. In practice, one can choose a readily interpretable (possibly less predictive) model. Another solution is to directly explain the original, highly predictive model. In this chapter, we present a middle-ground app

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Daniel R. Williams,Norman McIntyre of multiple models. Also, the top-ranked systems in many data-mining and computer vision competitions use ensembles. Although ensembles are popular, they are opaque and hard to interpret. Explanations make AI systems more transparent and also justify their predictions. However, there has been littl