Case-based reasoning and neural network based expert system for personalization

By Kwang Hyuk Im, Sang-Chan Park. 2007

68 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Eoin M. Kenny
2. Debashree Devi
3. Saroj Kumar Biswas
4. Akhil Kr. Das
5. Biswajit Purkayastha
6. Heisnam Rohen Singh
7. Manomita Chakraborty
8. Mark T. Keane

In Computer Vision and Pattern Recognition

1. Eoin M. Kenny
2. Mark T. Keane

Citations in the corpus

• The Twin-System Approach as One Generic Solution for XAI: An Overview of ANN-CBR Twins for Explaining Deep Learning (2019, background): “For the most part, this research does not involve controlled user-tests to determine the explanatory value of case-based explanations, but the authors did report that expert users were satisfied [Im and Park, 2007].”
• Hybrid case-based reasoning system by cost-sensitive neural network for classification (2016, methodology): “Sensitivity, activity saliency and relevance mechanisms are used in Shin and Park (1999), Shin et al. (2000), Im and Park (2007) and Park et al. (2004) to find symbolic feature weights from back-propagation neural network (BPNN).”
• How Case-Based Reasoning Explains Neural Networks: A Theoretical Analysis of XAI Using Post-Hoc Explanation-by-Example from a Survey of ANN-CBR Twin-Systems (2019, background): “Later work extends global weighting schemes to datasets having symbolic feature-values [53, 54].”