Communications of the ACM - Special issue on parallelism
Instance-based prediction of real-valued attributes
Computational Intelligence
Instance-Based Learning Algorithms
Machine Learning
Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms
International Journal of Man-Machine Studies - Special issue: symbolic problem solving in noisy and novel task environments
Case-based reasoning
Artificial Intelligence Review - Special issue on lazy learning
Artificial Intelligence Review - Special issue on lazy learning
Adaptation-guided retrieval: questioning the similarity assumption in reasoning
Artificial Intelligence
Inside Case-Based Reasoning
Locally Adaptive Metric Nearest-Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Prediction algorithms and confidence measures based on algorithmic randomness theory
Theoretical Computer Science - Natural computing
Transductive Confidence Machines for Pattern Recognition
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Reliable Classifications with Machine Learning
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Toward a Probabilistic Formalization of Case-Based Inference
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Constraint Classification: A New Approach to Multiclass Classification
ALT '02 Proceedings of the 13th International Conference on Algorithmic Learning Theory
Probabilistic Indexing for Case-Based Prediction
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
Developing Industrial Case-Based Reasoning Applications: The Inreca Methodology (Lecture Notes in Computer Science, 1612.)
Competence driven case-base mining
AAAI'05 Proceedings of the 20th national 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
Combining case-based and model-based reasoning for predicting the outcome of legal cases
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Generating estimates of classification confidence for a case-based spam filter
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
Learning similarity measures: a formal view based on a generalized CBR model
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
Conservative Adaptation in Metric Spaces
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Increasing Precision of Credible Case-Based Inference
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Belief Merging-Based Case Combination
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
A Model for Personalized Web-Scale Case Base Maintenance
AMT '09 Proceedings of the 5th International Conference on Active Media Technology
Uncertainty in clustering and classification
SUM'10 Proceedings of the 4th international conference on Scalable uncertainty management
Reexamination of CBR hypothesis
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
Fuzzy machine learning and data mininga
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
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In this paper, we propose a method for retrieving promising candidate solutions in case-based problem solving. Our method, referred to as credible case-based inference, makes use of so-called similarity profiles as a formal model of the key hypothesis underlying case-based reasoning (CBR), namely, the assumption that similar problems have similar solutions. Proceeding from this formalization, it becomes possible to derive theoretical properties of the corresponding inference scheme in a rigorous way. In particular, it can be shown that, under mild technical conditions, a set of candidates covers the true solution with high probability. Thus, the approach supports an important subtask in CBR, namely, to generate potential solutions for a new target problem in a sound manner and hence contributes to the methodical foundations of CBR. Due to its generality, it can be employed for different types of performance tasks and can easily be integrated in existing CBR systems.