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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Explainable and Interpretable Models in Computer Vision and Machine Learning

Explainable and Interpretable Models in Computer Vision and Machine Learning

Escalante, Hugo Jair
Escalera, Sergio
Guyon, Isabelle

Hardcover

Series: The Springer Challenges in Machine Learning

General ComputersProgramming

ISBN10: 3319981307
ISBN13: 9783319981307
Publisher: Springer Nature
Published: Jan 16 2019
Pages: 299
Weight: 1.40
Height: 0.81 Width: 6.39 Depth: 9.49
Language: English

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.

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