A plurality of binary support vector machines (SVM) are trained to vote between two of a plurality of classes. Each of the binary SVMs vote between a different pair of the plurality of classes. A set of validated samples is input to each of the binary SVMs. The set of validated samples includes samples of each of the classes. Likelihood estimates are determined based on votes of the binary SVMs. A prior probability distribution (prior) is assigned for each of the classes. A posterior probability is calculated for each of the classes based on the determined likelihood estimates and the assigned priors.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, comprising: training a plurality of binary support vector machines (SVM) to vote between two of a plurality of classes, each of the binary SVMs to vote between a different pair of the plurality of classes; inputting a set of validated samples to each of the binary SVMs, the set of validated samples to include samples of each of the classes; determining likelihood estimates based on votes of the binary SVMs in response to the inputting; assigning a prior probability distribution (prior) for each of the classes; and calculating a posterior probability for each of the classes based on the determined likelihood estimates and the assigned priors. 2. The method of claim 1, wherein determining the likelihood estimates includes, first determining a first number of votes for a first class of a pair of a first binary SVM of the plurality of binary SVMs, in response to inputted validated samples of the first class; second determining a second number of votes for a second class of the pair of the first binary SVM, in response to inputted validated samples of the first class; first dividing the first number of votes by the number of inputted validated samples of the first class to determine a first likelihood estimate of the first binary SVM voting the first class given a validated sample of the first class; and second dividing the second number of votes by the number of inputted validated samples of the first class to determine a second likelihood estimate of the first binary SVM voting for the second class given a validated sample of the first class. 3. The method of claim 2, wherein, first repeating the first determining, second determining, first dividing and the second dividing for each the remainder of the plurality of classes, using the validated samples of the corresponding remainder of the classes, to calculate a remainder of the likelihood estimates for the first binary SVM, and second repeating the first repeating for each of the remainder of the binary SVMs to determine likelihood estimates for each of classes for the remainder of the binary SVMs. 4. The method of claim 3, wherein calculating the posterior probability includes, inputting a first sample to each of binary SVMs, and determining a vote of each of the binary SVMs in response to the inputted first sample, the vote to indicate one of the pair of classes for each of the binary SVMs. 5. The method of claim 4, wherein calculating the posterior probability for the first class further includes determining a non-normalized probability by multiplying the prior of the first class by any of the likelihood estimates related to the binary SVMs that have pairs including the first class and that correspond to the determined vote for the binary SVM given a sample of the first class. 6. The method of claim 5, wherein calculating the posterior probability further includes normalizing the non-normalized probability, the normalizing to include, multiplying each of the priors with the likelihood estimate that the class of the prior is voted given the sample of the first class for one the binary SVMs having the pair including the first class; adding all of the multiplied priors to determine a first denominator; repeating the multiplying and the adding of the multiplied priors for any of a remainder of the binary SVMs that have pairs including the first class, to determine a remainder of a plurality of denominators; 7. The method of claim 6, wherein the normalizing further includes, multiplying the plurality of denominators; dividing the non-normalized probability by the multiplied plurality of denominators to determine a posterior probability for the first class that is normalized. 8. The method of claim 7, wherein, calculating the posterior probability further includes repeating the determining the non-normalized probability and determining the normalized probability for each of the remainder the plurality of classes hypothesized to be the first sample, given the likelihood estimates for the determined vote of the respective binary SVMs having pairs including the respective class, to determine a posterior probability for each of the remainder of the classes 9. The method of claim 8, wherein the priors are replaced with the corresponding posterior probabilities, for each of the classes, before a second sample is inputted to each of the binary SVMs. 10. The method of claim 1, wherein the set of validated samples is different than a set of training samples used during the training of the plurality of binary SVMS. 11. The method of claim 10, wherein, the set of training samples include a plurality of subclasses, at least two of the subclasses to correspond to one of the classes, and the training includes training at least one of the plurality of binary SVMs to distinguish between the two subclasses and to vote for the one class including the two subclasses, if at least one of the binary SVM identifies a sample as belonging to one of the two subclasses. 12. A system, comprising: a plurality of binary support vector machines (SVM) units to vote between two of a plurality of classes, each of the binary SVMs to vote between a different pair of the plurality of classes; a validated sample unit to input a set of validated samples to each of the binary SVMs, the set of validated samples to include samples of each of the classes; a posterior probability unit to calculate a posterior probability for each of the classes using Bayesian inference and based on likelihood estimates determined from votes of the binary SVMs in response to the set of validated samples. 13. The system of claim 10, further comprising: a likelihood estimate unit to determine the likelihood estimates for each class being voted in response to each class being input for each of the binary SVMs; and a prior unit to assign a prior probability distribution (prior) for each of the classes, wherein the posterior probability unit is to calculate the posterior probability for each of the classes further based on the priors. 14. A non-transitory computer-readable storage medium storing instructions that, if executed by a processor of a device, cause the processor to: run a set of validated samples against each of a plurality binary SVMs, each of the binary SVMs to vote between a different pair of classes of a plurality of classes in response to the input set of validated samples; determine likelihood estimates based on the votes of the binary SVMs; assign a prior probability distribution (prior) for each of the classes; and input the determined likelihood estimates and the assigned prior probabilities in to a Bayesian inference model to calculate a posterior probability for each of the classes. 15. The non-transitory computer-readable storage medium of claim 14, wherein, the classes of the validated samples is known, the set of validated samples include samples of each of the classes, and the priors of the plurality of classes are normalized to sum to one.