Learning from Low Training Data using Classifiers with Derivative Constraints

Availability of low training data presents a challenge in several learning scenarios, primarily since generalization is dependent on training using a large number of samples. However, there are several practical scenarios when limited data is available for training a classifier. In this paper, we present an approach for learning with few data samples, involving additional constraints based on computing derivatives of the decision boundary at the...