Ergebnisse für: Interpretable machine learning
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This book is written both for readers entering the field, and for practitioners with a background in AI and an interest in developing real-world applications. The book is a great resource for practitioners and researchers in both industry and academia, and the discussed case studies and associated m...
Die Entwicklung maschineller Lernverfahren für die computergestützte Suche in chemischen Datenbanken stellt ein hochaktuelles Forschungsthema in der Chemoinformatik dar. Bei der Entwicklung neuer Wirkstoffe kann die Anzahl der sonst erforderlichen, teuren experimentellen Analysen erheblich reduzie...
This book is written both for readers entering the field, and for practitioners with a background in AI and an interest in developing real-world applications. The book is a great resource for practitioners and researchers in both industry and academia, and the discussed case studies and associated m...
This book is written both for readers entering the field, and for practitioners with a background in AI and an interest in developing real-world applications. The book is a great resource for practitioners and researchers in both industry and academia, and the discussed case studies and associated m...
The book presents the peer-reviewed contributions of the 15th International Workshop on Self-Organizing Maps, Learning Vector Quantization and Beyond (WSOM$+$ 2024), held at the University of Applied Sciences Mittweida (UAS Mitt\-weida), Germany, on July 10–12, 2024.
The book highlights new develo...
The book presents the peer-reviewed contributions of the 15th International Workshop on Self-Organizing Maps, Learning Vector Quantization and Beyond (WSOM$+$ 2024), held at the University of Applied Sciences Mittweida (UAS Mitt\-weida), Germany, on July 10–12, 2024.
The book highlights new develo...
This book demonstrates the optimal adversarial attacks against several important signal processing algorithms. Through presenting the optimal attacks in wireless sensor networks, array signal processing, principal component analysis, etc, the authors reveal the robustness of the signal pro...
This open access book presents the concept of Informed Machine Learning and demonstrates its practical use with a compelling collection of applications of this paradigm in industrial and business use cases. These range from health care over manufacturing and material science to more advanced combina...
In an era where machine learning (ML) reshapes industries faster than terminology can standardize, The Dictionary of Applied Machine Learning emerges as an indispensable compass for navigating this dynamic discipline.
This rigorously curated reference offers not just definitions but contextual clar...
This book covers the latest research studies regarding Explainable Machine Learning used in multimedia-based healthcare applications. In this context, the content includes not only introductions for applied research efforts but also theoretical touches and discussions targeting open problems as well...
This book surveys the state of the art in explainable and interpretable reinforcement learning (RL) as relevant for robotics. While RL in general has grown in popularity and been applied to increasingly complex problems, several challenges have impeded the real-world adoption of RL algorithms for ro...
This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning.
Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like pe...
This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning.
Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like pe...
This book demonstrates the optimal adversarial attacks against several important signal processing algorithms. Through presenting the optimal attacks in wireless sensor networks, array signal processing, principal component analysis, etc, the authors reveal the robustness of the signal pro...
This book demonstrates the optimal adversarial attacks against several important signal processing algorithms. Through presenting the optimal attacks in wireless sensor networks, array signal processing, principal component analysis, etc, the authors reveal the robustness of the signal pro...
This book covers the latest research studies regarding Explainable Machine Learning used in multimedia-based healthcare applications. In this context, the content includes not only introductions for applied research efforts but also theoretical touches and discussions targeting open problems as well...
This book covers the latest research studies regarding Explainable Machine Learning used in multimedia-based healthcare applications. In this context, the content includes not only introductions for applied research efforts but also theoretical touches and discussions targeting open problems as well...
This book surveys the state of the art in explainable and interpretable reinforcement learning (RL) as relevant for robotics. While RL in general has grown in popularity and been applied to increasingly complex problems, several challenges have impeded the real-world adoption of RL algorithms for ro...
This book surveys the state of the art in explainable and interpretable reinforcement learning (RL) as relevant for robotics. While RL in general has grown in popularity and been applied to increasingly complex problems, several challenges have impeded the real-world adoption of RL algorithms for ro...
This open access book presents the concept of Informed Machine Learning and demonstrates its practical use with a compelling collection of applications of this paradigm in industrial and business use cases. These range from health care over manufacturing and material science to more advanced combina...