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Human and Machine Learning: Visible, Explainable, Trustworthy and Transparent

, human cognitive responses in ML-based decision making, human evaluation of machine learning and domain knowledge in transparent ML applications;

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Explainable, Transparent Autonomous Agents and Multi-Agent Systems

This book constitutes the proceedings of the Second International Workshop on Explainable, Transparent Autonomous Agents and Multi-Agent;

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Human and Machine Learning

models, human cognitive responses in ML-based decision making, human evaluation of machine learning and domain knowledge in transparent ML;

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Explainable AI with Python

This book provides a full presentation of the current concepts and available techniques to make machine learning systems more explainable;

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Explainable, Transparent Autonomous Agents and Multi-Agent Systems

This book constitutes the proceedings of the First International Workshop on Explainable, Transparent Autonomous Agents and Multi-Agent;

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Explainable AI Within the Digital Transformation and Cyber Physical Systems

understand and appropriately trust the obtained results. The authors discuss the challenges involved in making machine learning-based AI explainable;

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Artificial Intelligence in Medicine: Knowledge Representation and Transparent and Explainable Systems

for Health Care and Process-Oriented Information Systems in Health Care, and TEAAM 2019, the Workshop on Transparent, Explainable and;

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Rational Machines and Artificial Intelligence

Intelligent machines are populating our social, economic and political spaces. These intelligent machines are powered by Artificial;

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Machine Learning and Knowledge Discovery in Databases: International Workshops of Ecml Pkdd 2019, Würzburg, Germany, September 16-20, 2019, Proceeding

Learning and Knowledge Discovery in Databases, ECML PKDD, held in Wurzburg, Germany, in September 2019. The 70 full papers and 46 short papers;

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Explainable Artificial Intelligence Based on Neuro-Fuzzy Modeling with Applications in Finance

of artificial intelligence cannot ignore the need for interpretable, transparent, and explainable models. First, the main idea of the explainable;

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KI 2020: Advances in Artificial Intelligence

and topic areas of AI research. KI 2020 had a special focus on human-centered AI with highlights on AI and education and explainable machine;

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Classification in the Wild The Art and Science of Transparent Decision Making The Science and Art of Transparent Decision Making

rules that are simple, fast, and transparent and that can be as accurate as mathematically sophisticated algorithms developed for machine;

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Interpreting Machine Learning Models

Understand model interpretability methods and apply the most suitable one for your machine learning project. This book details the concepts;

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Explainable Fuzzy Systems

The importance of Trustworthy and Explainable Artificial Intelligence (XAI) is recognized in academia, industry and society. This book;

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Explainable Fuzzy Systems

The importance of Trustworthy and Explainable Artificial Intelligence (XAI) is recognized in academia, industry and society. This book;

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Machine Learning and Knowledge Discovery in Databases

Learning and Knowledge Discovery in Databases, ECML PKDD, held in Wurzburg, Germany, in September 2019. The 70 full papers and 46 short papers;

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Human-in-the-Loop Machine Learning

* Supervised machine learning design and query strategies to support Human-in-the-Loop systems * Advanced Adaptive Learning approaches;

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Natural Language Processing and Chinese Computing

This two-volume set of LNAI 11838 and LNAI 11839 constitutes the refereed proceedings of the 8th CCF Conference on Natural Language;

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Natural Language Processing and Chinese Computing

This two-volume set of LNAI 11838 and LNAI 11839 constitutes the refereed proceedings of the 8th CCF Conference on Natural Language;

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ECML PKDD 2020 Workshops: Workshops of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2020)

and Federated Learning, PDFL 2020; Second Workshop on Machine Learning for Cybersecurity, MLCS 2020, 9th International Workshop on New;

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Explainable Artificial Intelligence

learned by teachers and sustained effort. And what better way to strengthen the quality of AI and Machine learning outcomes. I hope that this book;

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Explainable Ai: Interpreting, Explaining and Visualizing Deep Learning

decisions. A limiting factor for a broader adoption of AI technology is the inherent risks that come with giving up human control and oversight to;

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Automated Machine Learning

Get to grips with automated machine learning and adopt a hands-on approach to AutoML implementation and associated methodologies Key;

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Explainable Recommendation

. It tries to address the problem of why: by providing explanations to users or system designers, it helps humans to understand why certain;

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Discovery Science

processing; ensembles; explainable and interpretable machine learning; graph and network mining; multi-target models; neural networks and deep;

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Machine Learning with R

learning book, updated and improved for R 3.6 and beyond Harness the power of R to build flexible, effective, and transparent machine learning;

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Adversarial Robustness for Machine Learning

Adversarial Robustness for Machine Learning summarizes the recent progress on this topic and introduces popular algorithms on;

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