標(biāo)題: Titlebook: Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning; Uday Kamath,John Liu Book 2021 The Editor(s) (if a [打印本頁(yè)] 作者: 共用 時(shí)間: 2025-3-21 17:10
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書(shū)目名稱(chēng)Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning影響因子(影響力)學(xué)科排名
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書(shū)目名稱(chēng)Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
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書(shū)目名稱(chēng)Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning讀者反饋
書(shū)目名稱(chēng)Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning讀者反饋學(xué)科排名
作者: 搖曳的微光 時(shí)間: 2025-3-21 21:27
Uday Kamath,John LiuSingle resource addressing the theory and practice of interpretability and explainability techniques using case studies.Covers exploratory data analysis, feature importance, interpretable algorithms, 作者: Ergots 時(shí)間: 2025-3-22 00:30 作者: 為現(xiàn)場(chǎng) 時(shí)間: 2025-3-22 08:06 作者: 準(zhǔn)則 時(shí)間: 2025-3-22 10:12
Exploratory Classification of Time-Series,ore effective models. Since any machine learning model is built from the data, understanding the content on which the model is based is imperative for explainability and interpretability. Many of these techniques that summarize, visualize, and explore data have existed for a long time. There have be作者: muster 時(shí)間: 2025-3-22 14:48
Suheir S. Sabbah,Bushra I. Albadawing of how well a model performs from looking at the results of model evaluation is another important way to enhance model explainability. We discuss several techniques to visualize model evaluation including precision-recall curves, ROC curves, residual plots, silhouette coefficients, and others to 作者: muster 時(shí)間: 2025-3-22 17:38 作者: 脆弱吧 時(shí)間: 2025-3-23 00:19 作者: 衍生 時(shí)間: 2025-3-23 01:34 作者: 要塞 時(shí)間: 2025-3-23 09:24 作者: 名詞 時(shí)間: 2025-3-23 11:36
way. The evaluation of explanations is an interdisciplinary research covering broad areas of human-computer interaction, machine learning, psychology, cognitive science, and visualization, to name a few. This chapter first highlights some of the recent works in research to categorize and analyze the作者: insert 時(shí)間: 2025-3-23 14:09
978-3-030-83358-9The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: intercede 時(shí)間: 2025-3-23 18:03 作者: antedate 時(shí)間: 2025-3-24 01:26
Participatory Ergonomics for Return to WorkP applications and the role of interpretability. Finally, we cover computer vision and how explainability has been a focus of considerable research. We will present a case study in each domain where the reader can get practical and real-world insights.作者: 粗野 時(shí)間: 2025-3-24 03:56 作者: Mast-Cell 時(shí)間: 2025-3-24 07:15
Explainability in Time Series Forecasting, Natural Language Processing, and Computer Vision,P applications and the role of interpretability. Finally, we cover computer vision and how explainability has been a focus of considerable research. We will present a case study in each domain where the reader can get practical and real-world insights.作者: ANN 時(shí)間: 2025-3-24 12:22 作者: grieve 時(shí)間: 2025-3-24 18:52
Post-Hoc Interpretability and Explanations,odel algorithms. They allow for different representations to be used for internal modeling and explanation. They can also provide different types of explanations for the same model. However, there is a trade-off between the fidelity and comprehensibility of explanations.作者: Palatial 時(shí)間: 2025-3-24 22:34
XAI: Challenges and Future, cognitive science, and visualization, to name a few. This chapter first highlights some of the recent works in research to categorize and analyze the metrics in a common framework. Finally, we give some predictions on the future based on current trajectories, commercial and open-source trends, and innovations in the field.作者: 殺蟲(chóng)劑 時(shí)間: 2025-3-25 01:09 作者: 口味 時(shí)間: 2025-3-25 06:24
Suheir S. Sabbah,Bushra I. Albadawiraditional machine learning models used in classification, regression, and clustering. The Pima Indian diabetes dataset is used to perform supervised and unsupervised classification. The insurance claims dataset is used for regression model analysis.作者: 泥瓦匠 時(shí)間: 2025-3-25 11:09 作者: 水槽 時(shí)間: 2025-3-25 11:47
Introduction to Interpretability and Explainability, us to trust the predictions of real-life applications of AI. Human-understandable explanations will encourage trust and continued adoption of machine learning systems as well as increasing system safety. As an emerging field, explainable AI will be vital for researchers and practitioners in the coming years.作者: Herpetologist 時(shí)間: 2025-3-25 17:22 作者: CROW 時(shí)間: 2025-3-25 23:31 作者: refraction 時(shí)間: 2025-3-26 00:33 作者: chronology 時(shí)間: 2025-3-26 07:30 作者: 斷言 時(shí)間: 2025-3-26 11:52
cognitive science, and visualization, to name a few. This chapter first highlights some of the recent works in research to categorize and analyze the metrics in a common framework. Finally, we give some predictions on the future based on current trajectories, commercial and open-source trends, and innovations in the field.作者: Density 時(shí)間: 2025-3-26 15:34 作者: seruting 時(shí)間: 2025-3-26 17:27 作者: evasive 時(shí)間: 2025-3-26 21:32 作者: Melanoma 時(shí)間: 2025-3-27 01:51 作者: 輕快帶來(lái)危險(xiǎn) 時(shí)間: 2025-3-27 07:10 作者: painkillers 時(shí)間: 2025-3-27 10:28 作者: Microaneurysm 時(shí)間: 2025-3-27 16:56
Explainability in Time Series Forecasting, Natural Language Processing, and Computer Vision, recent years. This chapter will discuss the research and applications of the interpretable and explainable algorithms in this domain. We will start with a time series algorithm survey, starting from traditional interpretable statistical models to modern deep learning algorithms. Next, we discuss NL作者: 流浪 時(shí)間: 2025-3-27 21:07 作者: 小臼 時(shí)間: 2025-3-28 00:36
Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning作者: Induction 時(shí)間: 2025-3-28 05:51
Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning978-3-030-83356-5作者: 過(guò)份好問(wèn) 時(shí)間: 2025-3-28 08:39
Exploratory Classification of Time-Series, data analysis on unstructured data such as text needs special handling as compared to structured data. This will entail a discussion on some of the well-known EDA techniques common to many NLP tasks. We will then discuss some of the feature engineering techniques that help us get more insights for 作者: organism 時(shí)間: 2025-3-28 14:22
