Recognition of handwritten and machine-printed text for postal address interpretation
Pattern Recognition Letters - Postal processing and character recognition
Machine Learning
Self-organizing maps
Refining Initial Points for K-Means Clustering
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Handwritten Numeral Recognition Using Gradient and Curvature of Gray Scale Image
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
A Formulation of Learning Vector Quantization Using a New Misclassification Measure
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
A Multi-Net Local Learning Framework for Pattern Recognition
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Adaptive mixtures of local experts
Neural Computation
Quantizing for minimum average misclassification risk
IEEE Transactions on Neural Networks
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This paper proposes a general local learning framework to effectively alleviate the complexities of classifier design by means of "divide and conquer" principle and ensemble method. The learning framework consists of quantization layer and ensemble layer. After GLVQ and MLP are applied to the framework, the proposed method is tested on public handwritten lowercase data sets, which obtains a promising performance consistently. Further, in contrast to LeNet5, an effective neural network structure, our method is especially suitable for a large-scale real-world classification problem although it is easily scaled to a small training set with preserving a good performance.