An important problem in pattern recognition is that of pattern classification. The objective of classification is to determine a discriminant function which is consistent with the given training examples and performs reasonably well on an unlabeled test set of examples. The degree of performance of the classifier on the test examples, known as its generalization performance, is an important issue in the design of the classifier. It has been established that a good generalization performance can be achieved by providing the learner with a sufficiently large number of discriminative training examples. However, in many domains, it is infeasible or expensive to obtain a sufficiently large training set. Various mechanisms have been proposed in literature to combat this problem. Active Learning techniques (Angluin, 1998; Seung, Opper, & Sompolinsky, 1992) reduce the number of training examples required by carefully choosing discriminative training examples. Bootstrapping (Efron, 1979; Hamamoto, Uchimura & Tomita, 1997) and other pattern synthesis techniques generate a synthetic training set from the given training set. We present some of these techniques and propose some general mechanisms for pattern synthesis.
Selection of Most Appropriate Backpropagation Training Algorithm in Data Pattern Recognition
