£68.06

Springer Explainable and Interpretable Models in Machine Learning

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Description

As computer vision and pattern recognition techniques reach near-human performance, the need to understand how these models function becomes a priority. This book from the Springer Series on Challenges in Machine Learning addresses the fundamental gap between high-performance modeling and model transparency. This collection compiles leading research focused on developing explainable and interpretable methods. It addresses the core questions facing modern AI: what is the rationale behind a specific decision, and how does the model structure explain its own functioning? While high performance is a requirement for modern systems, this text explores how to achieve that performance without sacrificing the ability to interpret results. Designed for researchers and professionals in the field, this work provides a deep look into the development of methods that make machine learning more transparent. It serves as a vital resource for anyone looking to move beyond black-box models toward systems that offer clear, understandable decision-making processes in computer vision and machine learning contexts.

Key Features

Provides a compilation of leading research on explainable and interpretable machine learning methods.

Addresses the critical need to understand the rationale behind decisions made by high-performance models.

Explores how model structures explain their own functioning within computer vision contexts.

Focuses on the transition from black-box modeling to transparent and interpretable AI techniques.

Part of the Springer Series on Challenges in Machine Learning for specialized academic study.

Connects advanced pattern recognition progress with the necessity for model explainability.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
16 January 2019
Listed Since
10 July 2018

Barcode

No barcode data available

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