Learning tradeoffs between discriminative power and invariance of classifiers

Abstract

Systems and methods are described for learning the discriminative power-invariance tradeoffs for classification of input data ('tradeoff learning system'). In various embodiments, the tradeoff learning system receives multiple classifiers ('base classifiers') and employs a learning technique to produce a combined classifier. Each received base classifier achieves a different level of tradeoff. The learning technique then decreases a function of kernel weights associated with each of the received classifiers to produce the combined classifier. By decreasing the function of kernel weights, the tradeoff learning system computes a combined classifier that classifies input data more accurately than the received multiple classifiers.