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Explainable and Interpretable Models in Computer Vision and Machine Learning, m. 1 Buch, m. 1 E-Book

Autor: Hugo Jair Escalante, Sergio Escalera, Isabelle Guyon, Xavier Baró, Yagmur Güçlütürk, Umut Güçlü, Marcel van Gerven

Język:

Oprawa: Twarda

Wydawca: Springer, Berlin

Dostępność: Dostępna u dostawcy

Wysyłamy za 14-21 dni

739.43 zł

This book compiles leading research on the development of explainable and interpretable machine lear...

Informacje o książce

Autor

Hugo Jair Escalante, Sergio Escalera, Isabelle Guyon, Xavier Baró, Yagmur Güçlütürk, Umut Güçlü, Marcel van Gerven

Język

Angielski

Oprawa

Książka - Twarda

Data wydania

2018

strony

299

EAN

9783319981307

ISBN

3319981307

Enbook ID

19776927

Wydawca

Springer, Berlin

Waga

673

Wymiary

155 x 235 x 21

Pełny opis

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 performance. Although these models have obtained astounding results, they are limited in their explainability and interpretability: what is the rationale behind the decision made? what in the model structure explains its functioning? Hence, while good performance is a critical required characteristic for learning machines, explainability and interpretability capabilities are needed to take learning machines to the next step to include them in decision support systems involving human supervision. This book, written by leading international researchers, addresses key topics of explainability and interpretability, including the following: · Evaluation and Generalization in Interpretable Machine Learning · Explanation Methods in Deep Learning · Learning Functional Causal Models with Generative Neural Networks · Learning Interpreatable Rules for Multi-Label Classification · Structuring Neural Networks for More Explainable Predictions · Generating Post Hoc Rationales of Deep Visual Classification Decisions · Ensembling Visual Explanations · Explainable Deep Driving by Visualizing Causal Attention · Interdisciplinary Perspective on Algorithmic Job Candidate Search · Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions · Inherent Explainability Pattern Theory-based Video Event Interpretations

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Explainable and Interpretable Models in Computer Vision and Machine Learning, m. 1 Buch, m. 1 E-Book

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