infection
发表于 2025-3-21 19:01:05
书目名称Explainable and Interpretable Models in Computer Vision and Machine Learning影响因子(影响力)<br> http://figure.impactfactor.cn/if/?ISSN=BK0319304<br><br> <br><br>书目名称Explainable and Interpretable Models in Computer Vision and Machine Learning影响因子(影响力)学科排名<br> http://figure.impactfactor.cn/ifr/?ISSN=BK0319304<br><br> <br><br>书目名称Explainable and Interpretable Models in Computer Vision and Machine Learning网络公开度<br> http://figure.impactfactor.cn/at/?ISSN=BK0319304<br><br> <br><br>书目名称Explainable and Interpretable Models in Computer Vision and Machine Learning网络公开度学科排名<br> http://figure.impactfactor.cn/atr/?ISSN=BK0319304<br><br> <br><br>书目名称Explainable and Interpretable Models in Computer Vision and Machine Learning被引频次<br> http://figure.impactfactor.cn/tc/?ISSN=BK0319304<br><br> <br><br>书目名称Explainable and Interpretable Models in Computer Vision and Machine Learning被引频次学科排名<br> http://figure.impactfactor.cn/tcr/?ISSN=BK0319304<br><br> <br><br>书目名称Explainable and Interpretable Models in Computer Vision and Machine Learning年度引用<br> http://figure.impactfactor.cn/ii/?ISSN=BK0319304<br><br> <br><br>书目名称Explainable and Interpretable Models in Computer Vision and Machine Learning年度引用学科排名<br> http://figure.impactfactor.cn/iir/?ISSN=BK0319304<br><br> <br><br>书目名称Explainable and Interpretable Models in Computer Vision and Machine Learning读者反馈<br> http://figure.impactfactor.cn/5y/?ISSN=BK0319304<br><br> <br><br>书目名称Explainable and Interpretable Models in Computer Vision and Machine Learning读者反馈学科排名<br> http://figure.impactfactor.cn/5yr/?ISSN=BK0319304<br><br> <br><br>
ostracize
发表于 2025-3-21 23:49:19
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–DOX
发表于 2025-3-22 00:49:48
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Carcinogen
发表于 2025-3-22 06:46:14
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
glans-penis
发表于 2025-3-22 12:29:09
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insurrection
发表于 2025-3-22 16:32:23
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insurrection
发表于 2025-3-22 18:53:59
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
爱好
发表于 2025-3-23 01:02:38
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
NAV
发表于 2025-3-23 04:38:51
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斜
发表于 2025-3-23 07:06:06
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