Interpretability
Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Suppor eBooks & eLearning
Posted by roxul at Oct. 24, 2019
Kenji Suzuki, "Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Suppor"
English | ISBN: 3030338495 | 2019 | 93 pages | PDF | 13 MB
English | ISBN: 3030338495 | 2019 | 93 pages | PDF | 13 MB
Interpretability of Computational Intelligence-Based Regression Models (Repost) eBooks & eLearning
Posted by AvaxGenius at June 11, 2017
Interpretability of Computational Intelligence-Based Regression Models By Tamás Kenesei, János Abonyi
English | PDF | 2015 | 89 Pages | ISBN : 3319219413 | 3 MB
The key idea of this book is that hinging hyperplanes, neural networks and support vector machines can be transformed into fuzzy models, and interpretability of the resulting rule-based systems can be ensured by special model reduction and visualization techniques. The first part of the book deals with the identification of hinging hyperplane-based regression trees.
Interpreting Machine Learning Models: Learn Model Interpretability and Explainability Methods eBooks & eLearning
Posted by hill0 at Jan. 1, 2022
Interpreting Machine Learning Models: Learn Model Interpretability and Explainability Methods
English | 2022 | ISBN: 1484278011 | 366 Pages | PDF EPUB | 24 MB
English | 2022 | ISBN: 1484278011 | 366 Pages | PDF EPUB | 24 MB
Interpretability of Machine Intelligence in Medical Image Computing eBooks & eLearning
Posted by DZ123 at Oct. 4, 2023
Mauricio Reyes, Pedro Henriques Abreu, Jaime Cardoso, "Interpretability of Machine Intelligence in Medical Image Computing"
English | 2022 | ISBN: 3031179757 | PDF | pages: 134 | 23.1 mb
English | 2022 | ISBN: 3031179757 | PDF | pages: 134 | 23.1 mb
An Introduction to Machine Learning Interpretability eBooks & eLearning
Posted by hill0 at Aug. 16, 2018
An Introduction to Machine Learning Interpretability
by Patrick Hall
English | 2018 | ISBN: 9781492033141 | 45 Pages | PDF | 4.45 MB
by Patrick Hall
English | 2018 | ISBN: 9781492033141 | 45 Pages | PDF | 4.45 MB
Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches eBooks & eLearning
Posted by hill0 at Oct. 22, 2022
Interpretability for Industry 4.0 : Statistical and Machine Learning Approaches
English | 2022 | ISBN: 3031124014 | 130 Pages | PDF EPUB (True) | 16 MB
English | 2022 | ISBN: 3031124014 | 130 Pages | PDF EPUB (True) | 16 MB
An Introduction to Machine Learning Interpretability - Dataiku version eBooks & eLearning
Posted by hill0 at Dec. 4, 2018
An Introduction to Machine Learning Interpretability - Dataiku version
by Navdeep Gill
English | 2018 | ISBN: 9781492050926 | 49 Pages | PDF | 4 MB
by Navdeep Gill
English | 2018 | ISBN: 9781492050926 | 49 Pages | PDF | 4 MB
Transparency and Interpretability for Learned Representations of Artificial Neural Networks eBooks & eLearning
Posted by hill0 at Nov. 29, 2022
Transparency and Interpretability for Learned Representations of Artificial Neural Networks
English | 2022 | ISBN: 365840003X | 333 Pages | PDF EPUB (True) | 43 MB
English | 2022 | ISBN: 365840003X | 333 Pages | PDF EPUB (True) | 43 MB
Interpretability of Computational Intelligence-Based Regression Models eBooks & eLearning
Posted by AvaxGenius at May 21, 2017
Interpretability of Computational Intelligence-Based Regression Models By Tamás Kenesei, János Abonyi
English | PDF | 2015 | 89 Pages | ISBN : 3319219413 | 3 MB
The key idea of this book is that hinging hyperplanes, neural networks and support vector machines can be transformed into fuzzy models, and interpretability of the resulting rule-based systems can be ensured by special model reduction and visualization techniques. The first part of the book deals with the identification of hinging hyperplane-based regression trees.
Interpretability of Computational Intelligence-Based Regression Models (Repost) eBooks & eLearning
Posted by AvaxGenius at July 18, 2017
Interpretability of Computational Intelligence-Based Regression Models By Tamás Kenesei, János Abonyi
English | PDF | 2015 | 89 Pages | ISBN : 3319219413 | 3 MB
The key idea of this book is that hinging hyperplanes, neural networks and support vector machines can be transformed into fuzzy models, and interpretability of the resulting rule-based systems can be ensured by special model reduction and visualization techniques. The first part of the book deals with the identification of hinging hyperplane-based regression trees.