Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Instance-Based Learning Algorithms
Machine Learning
Vector quantization and signal compression
Vector quantization and signal compression
A study of support vectors on model independent example selection
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
An efficient and scalable data compression approach to classification
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
A study of the behavior of several methods for balancing machine learning training data
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Quantizing for minimum average misclassification risk
IEEE Transactions on Neural Networks
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Borderline detection is the problem of finding samples falling near the decision boundary. It has many applications, related to the fact that for these samples small variations of feature values, due for instance to the presence of noise, can completely change their classification. In this paper, we propose an approach to borderline detection based on the geometric characteristics of labeled vector quantizers. The approach is based on the estimation of the true decision boundary by means of the Bayes Vector Quantizer (BVQ) algorithm. BVQ is a stochastic gradient algorithm for the minimization of the misclassification risk, hence it guarantees the accurate approximation of the optimal decision boundary. The features of the approach are discussed in comparison with Support Vector Machines (SVM), that is the best boundary hunting technique known in the literature.