Systems and method for object characterization include generating a classifier that defines a decision hyperplane separating a first classification region of a virtual feature space from a second classification region of the virtual feature space. Input information is provided to the classifier, and a number of classifications are received from the classifier. A distribution of the classifications is determined and used to generate a prediction.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method of object categorization, the method comprising: generating at least one classifier, the at least one classifier defining at least one decision hyperplane that separates a first classification region of a virtual feature space from a second classification region of the virtual feature space; providing input information to the at least one classifier; receiving, from the at least one classifier, a plurality classifications corresponding to the input information; determining a distribution of the plurality of classifications; and generating a prediction based on the distribution. 2. The method of claim 1, wherein determining a distribution of the plurality of classifications comprises characterizing the plurality of classifications using a histogram. 3. The method of claim 2, wherein characterizing the plurality of classifications using a histogram comprises: computing a plurality of distances features, wherein each of the plurality of distance features comprises a distance, in the virtual feature space, between one of the classifications and the hyperplane; and assigning each of the plurality of distance features to one of a plurality of bins of a histogram. 4. The method of claim 3, further comprising: determining a data density associated with a bin of the histogram; determining that the data density is below a threshold, wherein the threshold corresponds to a level of statistical significance; and modeling the distribution of data in the bin using a modeled distribution. 5. The method of claim 4, further comprising backfilling the bin with probabilities from the modeled distribution. 6. The method of claim 4, wherein the modeled distribution comprises a Cauchy distribution. 7. The method of claim 1, wherein generating the prediction comprises estimating, using a decision function, a probability associated with the distribution. 8. The method of claim 7, wherein the decision function utilizes at least one of Bayes estimation, positive predictive value (PPV) maximization, and negative predictive value (NPV) maximization. 9. The method of claim 1, wherein the at least one classifier comprises at least one of a support vector machine (SVM), an extreme learning machine (ELM), a neural network, a kernel-based perceptron, and a k-NN classifier. 10. The method of claim 1, further comprising generating the input information by extracting one or more features from a data set using one or more feature extractors. 11. The method of claim 10, wherein the data set comprises digital image data and wherein generating the prediction facilitates a pattern recognition process. 12. A system for object categorization, the system comprising: a memory having one or more computer-executable instructions stored thereon; and a processor configured to access the memory and to execute the computer-executable instructions, wherein the computer-executable instructions are configured to cause the processor, upon execution, to instantiate at least one component, the at least one component comprising: a classifier configured to receive input information, the classifier defining at least one decision hyperplane that separates a first classification region of a virtual feature space from a second classification region of the virtual feature space; a distribution builder configured to receive, from the classifier, a plurality of classifications corresponding to the input information, and to determine a distribution of the plurality of classifications; and a predictor configured to generate a prediction based on the distribution. 13. The system of claim 12, wherein the distribution builder is configured to determine the distribution by characterizing the plurality of classifications using a histogram. 14. The system of claim 13, wherein the distribution builder is configured to characterize the plurality of classifications using a histogram by: computing a plurality of distances features, wherein each of the plurality of distance features comprises a distance, in the virtual feature space, between one of the classifications and the hyperplane; and assigning each of the plurality of distance features to one of a plurality of bins of a histogram. 15. The system of claim 14, wherein the distribution builder is further configured to characterize the plurality of classifications using a histogram by: determining a data density associated with a bin of the histogram; determining that the data density is below a threshold, wherein the threshold corresponds to a level of statistical significance; and modeling the distribution of data in the bin using a modeled distribution. 16. The system of claim 15, wherein the distribution builder is further configured to characterize the plurality of classifications using a histogram by backfilling the bin with probabilities from the modeled distribution. 17. The system of claim 15, wherein the modeled distribution comprises a Cauchy distribution. 18. The system of claim 12, wherein the predictor is configured to generate the prediction by estimating, using a decision function, a probability associated with the distribution. 19. The system of claim 18, wherein the decision function utilizes at least one of Bayes estimation, positive predictive value (PPV) maximization, and negative predictive value (NPV) maximization. 20. The system of claim 12, wherein the at least one classifier comprises at least one of a support vector machine (SVM), an extreme learning machine (ELM), a neural network, a kernel-based perceptron, and a k-NN classifier. 21. The system of claim 12, further comprising a feature extractor configured to generate the input information by extracting one or more features from a data set. 22. The system of claim 21, wherein the data set comprises digital image data and wherein the predictor facilitates a pattern recognition process. 23. One or more computer-readable media having computer-executable instructions embodied thereon for object categorization, the instructions configured to be executed by a processor and to cause the processor, upon execution, to instantiate at least one component, the at least one component comprising: a classifier configured to receive input information, the classifier defining at least one decision hyperplane that separates a first classification region of a virtual feature space from a second classification region of the virtual feature space; a distribution builder configured to receive, from the classifier, a plurality of classifications corresponding to the input information, and to determine a distribution of the plurality of classifications; and a predictor configured to generate a prediction based on the distribution.