Explainable and Interpretable Models in Computer Vision and Machine Learning (The Springer Series on Challenges in Machine Learning)

Hugo Jair Escalante

ISBN 10: 3319981307 / ISBN 13: 9783319981307

Edité par Springer, 2019

A propos de cet article

Gebraucht - Sehr gut - ungelesen,als Mängelexemplar gekennzeichnet, mit leichten Mängeln an Schnitt oder Einband durch Lager- oder Transportschaden -This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning.Springer Fachmedien Wiesbaden GmbH, Abraham-Lincoln-Str. 46, 65189 Wiesbaden 316 pp. Englisch.

N° de réf. du vendeur INF1000251058

Détails bibliographiques

• Titre: Explainable and Interpretable Models in Computer Vision and Machine Learning (The Springer Series on Challenges in Machine Learning)
• Éditeur: Springer
• Date de parution: 2019
• Langue: anglais
• ISBN à 10 chiffres: 3319981307
• ISBN à 13 chiffres: 9783319981307
• Reliure: Gebundene Ausgabe
• État de l'article: Sehr gut

A propos de ce titre

Synopsis

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