A method for configuring a classifier includes operating the classifier to classify an input. The method also includes determining a confidence metric based on classification of the input. The method further includes dynamically updating a complexity of the classifier based on the confidence metric. The confidence metric may be computed based on a posterior probability. The complexity may be updated when the confidence metric is below a threshold value.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for configuring a classifier, comprising: operating the classifier to classify an input; determining a confidence metric based at least in part on classification of the input; and dynamically updating a complexity of the classifier based at least in part on the confidence metric. 2. The method of claim 1, in which the confidence metric is based at least in part on a posterior probability. 3. The method of claim 1, in which the dynamically updating comprises increasing the complexity of the classifier when the confidence metric is below a threshold value. 4. The method of claim 3, in which increasing the complexity comprises at least one of increasing a number of parameters for the classifier, changing values of existing parameters of the classifier, or a combination thereof. 5. The method of claim 3, in which increasing the complexity comprises changing an architecture of the classifier. 6. The method of claim 5, in which changing the architecture comprises at least one of increasing a number of convolution layers of the classifier, decreasing a stride of a convolution filter, or a combination thereof. 7. The method of claim 1, in which the updating comprises decreasing the complexity of the classifier when the confidence metric is above a threshold value. 8. An apparatus for configuring a classifier, comprising: a memory; and at least one processor coupled to the memory, the at least one processor being configured: to operate the classifier to classify an input; to determine a confidence metric based at least in part on classification of the input; and to dynamically update a complexity of the classifier based at least in part on the confidence metric. 9. The apparatus of claim 8, in which the at least one processor is further configured to determine the confidence metric based at least in part on a posterior probability. 10. The apparatus of claim 8, in which the at least one processor is further configured to dynamically update the complexity by increasing the complexity of the classifier when the confidence metric is below a threshold value. 11. The apparatus of claim 10, in which the at least one processor is further configured to increase the complexity by at least one of increasing a number of parameters for the classifier, changing values of existing parameters of the classifier, or a combination thereof. 12. The apparatus of claim 10, in which the at least one processor is further configured to increase the complexity by changing an architecture of the classifier. 13. The apparatus of claim 12, in which the at least one processor is further configured to change the architecture by at least one of increasing a number of convolution layers of the classifier, decreasing a stride of a convolution filter, or a combination thereof. 14. The apparatus of claim 8, in which the at least one processor is further configured to dynamically update the complexity by decreasing the complexity of the classifier when the confidence metric is above a threshold value. 15. An apparatus for configuring a classifier, comprising: means for operating the classifier to classify an input; means for determining a confidence metric based at least in part on classification of the input; and means for dynamically updating a complexity of the classifier based at least in part on the confidence metric. 16. The apparatus of claim 15, in which the confidence metric is based at least in part on a posterior probability. 17. The apparatus of claim 15, in which the updating means increases the complexity of the classifier when the confidence metric is below a threshold value. 18. The apparatus of claim 17, in which the updating means increases the complexity by at least one of increasing a number of parameters for the classifier, changing values of existing parameters of the classifier, or a combination thereof. 19. The apparatus of claim 17, in which the updating means increases the complexity by changing an architecture of the classifier. 20. The apparatus of claim 19, in which changing the architecture comprises at least one of increasing a number of convolution layers of the classifier, decreasing a stride of a convolution filter, or a combination thereof. 21. The apparatus of claim 15, in which the updating means decreases the complexity of the classifier when the confidence metric is above a threshold value. 22. A computer program product for configuring a classifier, comprising: a non-transitory computer readable medium having encoded thereon program code, the program code comprising: program code to operate the classifier to classify an input; program code to determine a confidence metric based at least in part on classification of the input; and program code to dynamically update a complexity of the classifier based at least in part on the confidence metric. 23. The computer program product of claim 22, further comprising program code to determine the confidence metric based at least in part on a posterior probability. 24. The computer program product of claim 22, further comprising program code to dynamically update the complexity by increasing the complexity of the classifier when the confidence metric is below a threshold value. 25. The computer program product of claim 24, in which the program code to increase the complexity comprises program code to at least one of increase a number of parameters for the classifier, change values of existing parameters of the classifier, or a combination thereof. 26. The computer program product of claim 24, in which the program code to increase the complexity comprises program code to increase the complexity by changing an architecture of the classifier. 27. The computer program product of claim 26, in which changing the architecture comprises at least one of increasing a number of convolution layers of the classifier, decreasing a stride of a convolution filter, or a combination thereof. 28. The computer program product of claim 22, further comprising program code to dynamically update the complexity by decreasing the complexity of the classifier when the confidence metric is above a threshold value.