Neural Computation
Local algorithms for pattern recognition and dependencies estimation
Neural Computation
Machine Learning
No free lunch for early stopping
Neural Computation
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Case-Based Reasoning: Experiences, Lessons and Future Directions
Case-Based Reasoning: Experiences, Lessons and Future Directions
Advances in Instance Selection for Instance-Based Learning Algorithms
Data Mining and Knowledge Discovery
Analysis of new techniques to obtain quality training sets
Pattern Recognition Letters - Special issue: Sibgrapi 2001
Navigating nets: simple algorithms for proximity search
SODA '04 Proceedings of the fifteenth annual ACM-SIAM symposium on Discrete algorithms
Cover trees for nearest neighbor
ICML '06 Proceedings of the 23rd international conference on Machine learning
Class Noise and Supervised Learning in Medical Domains: The Effect of Feature Extraction
CBMS '06 Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
A note on Platt's probabilistic outputs for support vector machines
Machine Learning
Fast Nearest Neighbor Condensation for Large Data Sets Classification
IEEE Transactions on Knowledge and Data Engineering
Mining competent case bases for case-based reasoning
Artificial Intelligence
Gaining insight through case-based explanation
Journal of Intelligent Information Systems
Fast Local Support Vector Machines for Large Datasets
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
An evaluation of the usefulness of case-based explanation
ICCBR'03 Proceedings of the 5th international conference on Case-based reasoning: Research and Development
Noise reduction for instance-based learning with a local maximal margin approach
Journal of Intelligent Information Systems
Fast and Scalable Local Kernel Machines
The Journal of Machine Learning Research
Adaptive case-based reasoning using retention and forgetting strategies
Knowledge-Based Systems
Operators for transforming kernels into quasi-local kernels that improve SVM accuracy
Journal of Intelligent Information Systems
Profiling instances in noise reduction
Knowledge-Based Systems
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Because case-based reasoning (CBR) is instance-based, it is vulnerable to noisy data. Other learning techniques such as support vector machines (SVMs) and decision trees have been developed to be noise-tolerant so a certain level of noise in the data can be condoned. By contrast, noisy data can have a big impact in CBR because inference is normally based on a small number of cases. So far, research on noise reduction has been based on a majority-rule strategy, cases that are out of line with their neighbors are removed. We depart from that strategy and use local SVMs to identify noisy cases. This is more powerful than a majority-rule strategy because it explicitly considers the decision boundary in the noise reduction process. In this paper we provide details on how such a local SVM strategy for noise reduction can be made scale to very large datasets ( 500,000 training samples). The technique is evaluated on nine very large datasets and shows excellent performance when compared with alternative techniques.