Modified Quadratic Discriminant Functions and the Application to Chinese Character Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive pattern recognition and neural networks
Adaptive pattern recognition and neural networks
The nature of statistical learning theory
The nature of statistical learning theory
A Survey of Methods and Strategies in Character Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern classification: a unified view of statistical and neural approaches
Pattern classification: a unified view of statistical and neural approaches
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution locally expanded HONN for handwritten numeral recognition
Pattern Recognition Letters
Example-Based Learning for View-Based Human Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic Recognition of Handwritten Numerical Strings: A Recognition and Verification Strategy
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handwritten Numerical Recognition Using Autoassociative Neural Networks
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 1 - Volume 1
Adaptation in Statistical Pattern Recognition Using Tangent Vectors
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
The PDF projection theorem and the class-specific method
IEEE Transactions on Signal Processing
Integrated segmentation and recognition of handwritten numeralswith cascade neural network
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Neural and statistical classifiers-taxonomy and two case studies
IEEE Transactions on Neural Networks
Modeling the manifolds of images of handwritten digits
IEEE Transactions on Neural Networks
Discriminative learning quadratic discriminant function for handwriting recognition
IEEE Transactions on Neural Networks
Developing higher-order networks with empirically selected units
IEEE Transactions on Neural Networks
Partial Discriminative Training of Neural Networks for Classification of Overlapping Classes
ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
Facial expression recognition on multiple manifolds
Pattern Recognition
Expert Systems with Applications: An International Journal
Precise and accurate decimal number recognition using Global Motion Estimation
International Journal of Artificial Intelligence and Soft Computing
Expert Systems with Applications: An International Journal
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The polynomial classifier (PC) that takes the binomial terms of reduced subspace features as inputs has shown superior performance to multilayer neural networks in pattern classification. In this paper, we propose a class-specific feature polynomial classifier (CFPC) that extracts class-specific features from class-specific subspaces, unlike the ordinary PC that uses a class-independent subspace. The CFPC can be viewed as a hybrid of ordinary PC and projection distance method. The class-specific features better separate one class from the others, and the incorporation of class-specific projection distance further improves the separability. The connecting weights of CFPC are efficiently learned class-by-class to minimize the mean square error on training samples. To justify the promise of CFPC, we have conducted experiments of handwritten digit recognition and numeral string recognition on the NIST Special Database 19 (SD19). The digit recognition task was also benchmarked on two standard databases USPS and MNIST. The results show that the performance of CFPC is superior to that of ordinary PC, and is competitive with support vector classifiers (SVCs).