Algorithms for clustering data
Algorithms for clustering data
Instance-Based Learning Algorithms
Machine Learning
From global similarities to kinds of similarities: the construction of dimensions in development
Similarity and analogical reasoning
Artificial Intelligence Review - Special issue on lazy learning
Multidimensional binary search trees used for associative searching
Communications of the ACM
Dynamic Memory: A Theory of Reminding and Learning in Computers and People
Dynamic Memory: A Theory of Reminding and Learning in Computers and People
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Concepts Learning with Fuzzy Clustering and Relevance Feedback
MLDM '01 Proceedings of the Second International Workshop on Machine Learning and Data Mining in Pattern Recognition
On the Role of Abstraction in Case-Based Reasoning
EWCBR '96 Proceedings of the Third European Workshop on Advances in Case-Based Reasoning
Retrieval in a Prototype-Based Case Library: A Case Study in Diabetes Therapy Revision
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
The Life Cycle of Test Cases in a CBR System
EWCBR '00 Proceedings of the 5th European Workshop on Advances in Case-Based Reasoning
Learning Feature Weights from Case Order Feedback
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Case-Based Reasoning Technology, From Foundations to Applications
ECCBR '02 Proceedings of the 6th European Conference on Advances in Case-Based Reasoning
Why Case-Based Reasoning Is Attractive for Image Interpretation
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Temporal Abstractions and Case-Based Reasoning for Medical Course Data: Two Prognostic Applications
MLDM '01 Proceedings of the Second International Workshop on Machine Learning and Data Mining in Pattern Recognition
Case-Based Reasoning in CARE-PARTNER: Gathering Evidence for Evidence-Based Medical Practice
EWCBR '98 Proceedings of the 4th European Workshop on Advances in Case-Based Reasoning
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
Medical applications in case-based reasoning
The Knowledge Engineering Review
Emergent case-based reasoning applications
The Knowledge Engineering Review
Image processing in case-based reasoning
The Knowledge Engineering Review
The Dissimilarity Representation for Pattern Recognition: Foundations And Applications (Machine Perception and Artificial Intelligence)
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
Case-base maintenance by conceptual clustering of graphs
Engineering Applications of Artificial Intelligence
Introspective learning to build case-based reasoning (CBR) knowledge containers
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Concepts for novelty detection and handling based on a case-based reasoning process scheme
ICDM'07 Proceedings of the 7th industrial conference on Advances in data mining: theoretical aspects and applications
An adaptive nearest neighbor classification algorithm for data streams
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Improving the K-NN classification with the euclidean distance through linear data transformations
ICDM'04 Proceedings of the 4th international conference on Advances in Data Mining: applications in Image Mining, Medicine and Biotechnology, Management and Environmental Control, and Telecommunications
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Case-based reasoning (CBR) solves problems using the already stored knowledge, and captures new knowledge, making it immediately available for solving the next problem. Therefore, CBR can be seen as a method for problem solving, and also as a method to capture new experience and make it immediately available for problem solving. The CBR paradigm has been originally introduced by the cognitive science community. The CBR community aims to develop computer models that follow this cognitive process. Up to now many successful computer systems have been established on the CBR paradigm for a wide range of real-world problems. We will review in this paper the CBR process and the main topics within the CBR work. Hereby we try bridging between the concepts developed within the CBR community and the statistics community. The CBR topics we describe are: similarity, memory organization, CBR learning, and case-base maintenance. Then we will review based on applications the open problems that need to be solved. The applications we are focusing on are meta-learning for parameter selection, image interpretation, incremental prototype-based classification and novelty detection and handling. Finally, we summarize our concept on CBR.