The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Machine Learning
Making large-scale support vector machine learning practical
Advances in kernel methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Incorporating Invariances in Support Vector Learning Machines
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
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A new method for recognition of isolated handwritten English digits is presented here. This method is based on Support Vector Machines (SVMs). Mean and standard deviation of each digit is considered as the features. Using these features, multiple SVM classifiers are trained to separate different classes of digits. Support vector machine are based on the concept of decision planes that defines the decision boundaries. The decision plane is one that separates between the set of digits having different class membership. The approach works in four steps 1) Preprocessing 2) Feature extraction 3) Classification 4) detection. A database of 100 different representation of each digit is constructed for the training database. The digits are first manually segmented into 5 classes to minimize the time required to obtain the hyperplane. Then the input is again check against the two classes by 2-class SVM classifier. Experiments show that the proposed features can provide a very good recognition result using Support Vector Machines at a recognition rate 97%, compared with 91.25% obtained by MLP neural network classifier using the same features and test set.