1. A method performed by a computing system for learning tradeoffs between discriminative power and invariance of classifiers, comprising: receiving two or more classifiers, each classifier for classifying data and having associated therewith a kernel with corresponding kernel weight, the kernel specifying an attribute for its associated classifier; employing a learning technique to produce a combined classifier based on the two or more received classifiers by decreasing a function of the kernel weights to learn tradeoffs between discriminative power and invariance; generating by the computing system a base kernel for each of the received two or more classifiers; and selecting one of the generated base kernels by solving a convex optimization problem having a linear objective and quadratic inequality constraints.
1. A method performed by a computing system for learning tradeoffs between discriminative power and invariance of classifiers, comprising: receiving two or more classifiers, each classifier for classifying data and having associated therewith a kernel with corresponding kernel weight, the kernel specifying an attribute for its associated classifier; employing a learning technique to produce a combined classifier based on the two or more received classifiers by decreasing a function of the kernel weights to learn tradeoffs between discriminative power and invariance; generating by the computing system a base kernel for each of the received two or more classifiers; and selecting one of the generated base kernels by solving a convex optimization problem having a linear objective and quadratic inequality constraints. 4. The method of claim 1 , further comprising determining a proportion in which at least one of the received two or more classifiers should be combined.