Instance-based prediction of real-valued attributes
Computational Intelligence
Instance-Based Learning Algorithms
Machine Learning
Case-based reasoning
Lazy learning
Case-Based Reasoning for Multi-Step Problems and Its Integration with Heuristic Search
EWCBR '94 Selected papers from the Second European Workshop on Advances in Case-Based Reasoning
The Utility Problem Analysed: A Case-Based Reasoning Perspective
EWCBR '96 Proceedings of the Third European Workshop on Advances in Case-Based Reasoning
Building Compact Competent Case-Bases
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
Toward a Probabilistic Formalization of Case-Based Inference
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Probabilistic Indexing for Case-Based Prediction
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
Version spaces: a candidate elimination approach to rule learning
IJCAI'77 Proceedings of the 5th international joint conference on Artificial intelligence - Volume 1
Remembering to forget: a competence-preserving case deletion policy for case-based reasoning systems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
A logical approach to case-based reasoning using fuzzy similarity relations
Information Sciences: an International Journal
| Hi-index | 0.00 |
In order to predict the solution to a new problem we proceed from the "similar problem-similar solution" assumption underlying case-based reasoning. The concept of a similarity hypothesis is introduced as a formal model of this meta-heuristic. It allows for realizinga constraint-based inference scheme which derives a prediction in the form of a set of possible candidates. We propose an algorithm for learning a suitable similarity hypothesis from a sequence of observations. Basing the inference process on hypotheses thus defined yields (set-valued) predictions that cover the true solution with high probability. Our method is meant to support the overall (case-based) problem solving process by bringing a promising set of possible solutions into focus.