Learning tradeoffs between discriminative power and invariance of classifiers
AssigneeMicrosoft Corporation
InventorsManik Varma, Debajyoti Ray
Life of this patent
Estimating the term…
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.
Claims17
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.
A system for learning tradeoffs between discriminative power and invariance of classifiers, comprising:
- a processor and memory;
- two or more classifiers, each classifier for classifying input and having associated therewith a kernel with a corresponding kernel weight, the kernel specifying an attribute for its associated classifier;
- a tradeoff learning component configured to combine the two or more classifiers to produce a combined classifier by decreasing a function of the kernel weights, wherein each classifier is a support vector machine incorporating a first tradeoff between discriminative power and invariance, and the produced combined classifier incorporates a second tradeoff between discriminative power that is different from the first tradeoff between discriminative power;
- and
- a classifier component configured to receive an input and classifies the received input using the produced combined classifier.
The system of claim wherein the function of the kernel weights can be expressed as:
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A computer-readable storage device storing computer-readable instructions that, when executed, perform a method for learning tradeoffs between discriminative power and invariance for classifiers, the method comprising:
- receiving two or more classifiers, each classifier for classifying input and having associated therewith an attribute;
- generating a base kernel for each of the received classifiers, the base kernel specifying a function of the attribute, each base kernel having one or more weights;
- and
- producing a combined classifier based on at least the generated base kernels wherein the produced combined classifier classifies the input with greater precision than the received classifiers, wherein the combined classifier incorporates a tradeoff between discriminative power and invariance that is different from at least one of the received two or more classifiers.
Dependencies are inferred from the claim text.