Systems, methods, and media for updating a classifier are provided, in some embodiments, systems for updating a classifier are provided, the systems comprising: a hardware processor that is configured to: receive a sample; for each of a first plurality of weak learners, classify the sample using the weak learner, determine an outcome of the classification, and determine an up-dated error rate of the weak learner based on the outcome of the classification and at least one of: (i) a count of positive samples used to update the classifier, and (ii) a count of negative samples used to update the classifier; select a first weak learner from the first plurality of weak learners based on the updated error rate of the first weak learner; and update tire classifier based on the first weak learner.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for updating a classifier, comprising: a hardware processor that is con figured to: receive a sample; for each of a first plurality of weak learners, classify the sample using the weak learner, determine an outcome of the classification, and determine an updated error rate of the weak learner based on the outcome of the classification and at least one of: (i) a count of positive samples used to update the classifier, and (ii) a count of negative samples used to update the classifier; select a first weak learner from the first plurality of weak learners based on the updated error rate of the first weak learner; and update the classifier based on the first weak learner. 2. The system of claim 1, wherein the hardware processor is further configured to: assign a first importance weight to the sample based on the outcome of the classification of the sample by the first weak learner and a count of samples that have been used to update the classifier; for each of a second plurality of weak learners, classify the sample using the weak learner, determine an outcome of the classification, and determine an updated error rate of the weak learner based on the first importance weight; select a second weak learner from the second plurality based on the updated error rate of the second weak learner; and update the classifier based on the second weak learner. 3. The system of claim 2, wherein the first importance weight is determined based on a ratio between the count of positive samples and a count of all samples that have been used to update the classifier. 4. The system of claim 2, wherein the first importance weight is determined based on a ratio between the count of negative samples and a count of all samples that have been used to update the classifier. 5. The system if claim 1, wherein the classifier is a linear classifier having the form: F(x)=sign{Σα×h(x)}, where h(x) is an output returned by a weak learner h for a data sample x and α is a voting weight. 6. The system of claim 1, wherein each weak learner in the first plurality is based on different Haar feature. 7. The system of claim 1, wherein the count of positive samples includes a count of positive samples that have been used to update the classifier prior to the receipt of the sample. 8. A system for updating a classifier, comprising: a hardware processor that is configured to: receive a sample; assign a first importance weight to the sample based on a count of samples used to update the classifier; for each of a first plurality of weak learners, classify the sample using the weak learner, determine an outcome of the classification, and determine an updated error rate of the weak learner based on the outcome of the classification and the first importance weight; select a first weak learner from the first plurality of weak learners based on the updated error rate of the first weak learner; and update the classifier based on the first weak learner. 9. The system of claim 8, wherein the classifier is a linear classifier having the form: F(x)=sign{Σα×h(x)}, where h(x) is an output returned b a weak learner h for a data sample x and α is a voting weight. 10. The system of claim 8, wherein: the first plurality of weak learners is associated with a first selector, the first importance weight is determined by a second selector associated with a second plurality of weak learners, the first importance weight is determined based on a classification of the sample by a second weak learner from the second plurality that is performed prior to the classification of the sample by the first weak learner from the first plurality, and the first importance weight is used by the first selector to calculate error rates of weak learners from the first plurality. 11. The system of claim 8, wherein the first importance weight is determined based on a ratio between a ennui of negative samples used to update the classifier and a count of all samples used to update the classifier. 12. The system of claim 8, wherein the first importance weight is determined based on a ratio between a count of positive samples used to update the classifier and a count of all samples used to update the classifier. 13. The system of claim 8, wherein each weak learner in the first plurality is based on a different Haar feature. 14. The system of claim 8, wherein the sample is a medical imaging image. 15. A method for updating a classifier, comprising: receiving a sample; for each of a first plurality of weak learners, classifying the sample using the weak learner, determining an outcome of the classification, and determining, by a hardware processor, an updated error rate of the weak learner based on the outcome of the classification and at least one of: (i) a count of positive samples used to update the classifier, and (ii) a count of negative samples used to update the classifier; selecting a first weak learner from the first plurality of weak learners based on the updated error rate of the first weak learner; and updating the classifier based on the first weak learner. 16. The method of claim 15, further comprising: assigning a first importance weight to the sample based on the outcome of the classification of the sample by the first weak learner and a count of samples used to update the classifier; for each of a second plurality of weak learners, classifying the sample using the weak learner, determining an outcome of the classification, and determining an updated error rate of the weak learner based on the first importance weight; selecting a second weak learner from the second plurality based on the updated error rate of the second weak learner; and updating the classifier based on the second weak learner. 17. The method of claim 16, wherein the first importance weight is determined based on a ratio between the count of positive samples and a count of all samples used to update the classifier. 18. The method of claim 16, wherein the first importance weight is determined based on a ratio between the count of negative samples and a count of all samples used to update the classifier. 19. The method of claim 15, wherein the classifier is a linear classifier having the form: F(x)=sign{Σα×h(x)}, where h(x) is an output returned by a weak learner h for a data sample x and α is a voting weight. 20. The method of claim 15, wherein each weak learner in the first plurality is based on a different Haar feature. 21. The method of claim 15, wherein the count of positive samples used to update the classifier includes a count of positive samples that have been used to update the classifier prior to the receipt of the sample. 22. A method for updating a classifier, comprising: receiving a sample; assigning a first importance weight to the sample based on a count of samples used to update the classifier; for each of a first plurality of weak learners, classifying the sample using the weak learner, determining an outcome of the classification, and determining, by a hardware processor, an updated error rate of the weak learner based on the outcome of the classification and the first importance weight; selecting a first weak learner from the first plurality based on the updated error rate of the first weak learner; and updating the classifier based on the first weak learner. 23. The method of claim 22, wherein the classifier is a linear classifier having the form: F(x)=sign{Σα×h(x)}, where h(x) is an output returned by a weak learner h for a data sample x and α is a voting weight. 24. The method of claim 22, wherein: the first plurality of weak learners is associated with a first selector, the first importance weight is determined by a second selector associated with a second plurality of weak learners, the first importance weight is determined based on a classification of the sample by a second weak learner from the second plurality that is performed prior to the classification of the sample by the first weak learner from the first plurality, and the first importance weight is used by the first selector to calculate error rates of weak learners from the first plurality. 25. The method of claim 22, wherein the first importance weight is determined based on a ratio between a count of negative samples used to update the classifier and a count of all samples used to update the classifier. 26. The method of claim 22, wherein the first importance weight is determined based on a ratio between a count of positive samples used to update the classifier and a count of all samples used to update the classifier. 27. The method of claim 22, wherein each weak learner in the first plurality is based on a different Haar feature. 28. The method of claim 22, wherein the sample is a medical imaging image. 29. A non-transitory computer-readable medium containing computer-executable instructions that, when executed by a processor, cause the processor to perform a method for updating a classifier, the method comprising: receiving a sample; for each of a first plurality of weak learners, classifying the sample using the weak learner, determining an outcome of the classification, and determining an updated error rate of the weak learner based on the outcome of the classification and at least one of: (i) a count of positive samples used to update the classifier, and (ii) a count of negative samples used to update the classifier; selecting a first weak learner from the first plurality of weak learners based on the updated error rate of the first weak learner; and updating the classifier based on the first weak learner. 30. The non-transitory computer-readable medium of claim 29, further comprising: assigning a first importance weight to the sample based on the outcome of the classification of the sample by the first weak learner and a count of samples used to update the classifier; for each of a second plurality of weak learners, classifying the sample using the weak learner, determining an outcome of the classification, and determining an updated error rate of the weak learner based on the first importance weight; selecting a second weak learner from the second plurality based on the updated error rate of the second weak learner; and updating the classifier based on the second weak learner. 31. The non-transitory computer-readable medium of claim 30, wherein the first importance weight is determined based on a ratio between the count of positive samples and a count of all samples used to update the classifier. 32. The non-transitory computer-readable medium of claim 30, wherein the first importance weight is determined based on a ratio between the count of negative samples and a count of all samples used to update the classifier. 33. The non-transitory computer-readable medium of claim 29, wherein the classifier is a linear classifier having the form: F(x)=sign{Σα×h(x)}, where h(x) is an output returned by a weak learner h for a data sample x and α is a voting weight. 34. The non-transitory computer-readable medium of claim 29, wherein each weak learner in the first plurality is based on a different Haar feature. 35. The non-transitory computer-readable medium of claim 29, wherein the count of positive samples used to update the classifier includes a count of positive samples that have been used to update the classifier prior to the receipt of the sample. 36. A non-transitory computer-readable medium containing computer-executable instructions that, when executed by a processor, cause the processor to perform a method for updating a classifier, the method comprising: receiving a sample; assigning a first importance weight to the sample based On a count of samples used to update the classifier; for each of a first plurality of weak learners, classifying the sample using the weak learner, determining an outcome of the classification, and determining, an updated error rate of the weak learner based on the outcome of classification and the first importance weight; selecting a first weak learner from the first plurality of weak learners based on the updated error rate of the first weak learner; and updating the classifier based on the first weak learner. 37. The non-transitory computer-readable medium of claim 36, wherein the classifier is a linear classifier having the form: F(x)=sign{Σα×h(x)}, where h(x) is an output returned by a weak learner h for a data sample x and α is a voting weight. 38. The non-transitory computer-readable medium of claim 36, wherein: the first plurality of weak learners is associated with a first selector, the first importance weight is determined by a second selector associated with a second plurality of weak learners, the first importance weight is determined based on a classification of the sample by a second weak learner from the second plurality that is performed prior to the classification of the sample by the first weak learner from the first plurality, and the first importance weight is used by the first selector to calculate error rates of weak learners from the first plurality. 39. The non-transitory computer-readable medium of claim 36, wherein the first importance weight is determined based on a ratio between a count of negative samples used to update the classifier and a count of all samples used to update the classifier. 40. The non-transitory computer-readable medium of claim 36, wherein the first importance weight is determined based on a ratio between a count of positive samples used to update the classifier and a count of all samples used to update the classifier. 41. The non-transitory computer-readable medium of claim 36, wherein each weak learner in the first plurality is based on a different Haar feature. 42. The non-transitory computer-readable medium of claim 36, wherein the sample is a medical imaging image.