Initializing back propagation networks with prototypes
Neural Networks
A new definition of neighborhood of a point in multi-dimensional space
Pattern Recognition Letters
On the use of neighbourhood-based non-parametric classifiers
Pattern Recognition Letters - special issue on pattern recognition in practice V
Performance Evaluation of the Nearest Feature Line Method in Image Classification and Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the use of nearest feature line for speaker identification
Pattern Recognition Letters
The Pattern Classification Based on the Nearest Feature Midpoints
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Rapid and brief communication: Center-based nearest neighbor classifier
Pattern Recognition
Rectified nearest feature line segment for pattern classification
Pattern Recognition
Face recognition using the nearest feature line method
IEEE Transactions on Neural Networks
Pattern Recognition Letters
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A new approach called shortest feature line segment (SFLS) is proposed to implement pattern classification in this paper, which can retain the ideas and advantages of nearest feature line (NFL) and at the same time can counteract the drawbacks of NFL. The proposed SFLS uses the length of the feature line segment satisfying given geometric relation with query point instead of the perpendicular distance defined in NFL. SFLS has clear geometric-theoretic foundation and is relatively simple. Experimental results on some artificial datasets and real-world datasets are provided, together with the comparisons between SFLS and other neighborhood-based classification methods, including nearest neighbor (NN), k-NN, NFL and some refined NFL methods, etc. It can be concluded that SFLS is a simple yet effective classification approach.