Examining the Impact of Network Architecture on Extracted Feature Quality for CBR

Leake, David; Wilkerson, Zachary; Vats, Vibhas; Acharya, K. Aniruddha; Crandall, David J.

doi:10.1007/978-3-031-40177-0_1

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2024

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Cited by 3 publications

References 22 publications

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Extracting Indexing Features for CBR from Deep Neural Networks: A Transfer Learning Approach

Wilkerson,

Leake,

Vats

et al. 2024

Case-Based Reasoning Research and Development

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Extracting Indexing Features for CBR from Deep Neural Networks: A Transfer Learning Approach

Wilkerson,

Leake,

Vats

et al. 2024

Case-Based Reasoning Research and Development

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An Empirical Analysis of User Preferences Regarding XAI Metrics

Darias,

Bayrak,

Caro-Martínez

et al. 2024

Case-Based Reasoning Research and Development

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A Review of the Development and Future Challenges of Case-Based Reasoning

Yan,

Cheng

2024

Applied Sciences

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Case-based reasoning (CBR), which is based on the cognitive assumption that similar problems have similar solutions, is an important problem-solving and learning method in the field of artificial intelligence (AI). In this article, the development of CBR is reviewed, and the major challenges of CBR are summarized. The paper is organized into four parts. First, the basic framework and concepts of CBR are introduced. Then, the developed technology and innovative work that were designed to solve problems by CBR are summarized. Then, the application fields of CBR are summarized. Finally, according to the idea of deep learning and interpretable AI, the main challenges for the future development of CBR are proposed.

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Examining the Impact of Network Architecture on Extracted Feature Quality for CBR

Cited by 3 publications

References 22 publications

Extracting Indexing Features for CBR from Deep Neural Networks: A Transfer Learning Approach

Extracting Indexing Features for CBR from Deep Neural Networks: A Transfer Learning Approach

An Empirical Analysis of User Preferences Regarding XAI Metrics

A Review of the Development and Future Challenges of Case-Based Reasoning

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