In some examples, a computing system may gathering, from a machine learning unit associated with the computing system, data as a training data, label the data to identify the labeled data as the training data, which may be then recirculated for each of one or more analytics modules of the machine learning unit.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method, comprising: gathering, from a machine learning unit, data as a training data; labeling the data to identify the labeled data as the training data; and recirculating the labeled data for each of one or more analytics modules of the machine learning unit. 2. The method of claim 1, wherein the gathering comprises sifting classified data from the machine learning unit to select some of the classified data that is known to be valid as the training data. 3. The method of claim 1, wherein the recirculating comprises: evaluating outputs of a plurality of analytics modules of the machine learning unit; and identifying a list of the one or more analytics modules for training. 4. The method of claim 3, wherein the identifying comprises identifying the one or more analytics modules for training as a result of an output of each of the one or more analytics modules being invalid. 5. The method of claim 3, wherein the evaluating comprises determining an output of a first analytics module of the one or more analytics modules to be valid following a recirculation of the training data through the first analytics module, and wherein the identifying comprises excluding the first analytics module from the list of one or more analytics modules for training. 6. The method of claim 3, wherein the recirculating further comprises: for each of the one or more analytics modules, calculating a respective number of times that the training data is to be recirculated to the respective analytics module for training. 7. The method of claim 6, wherein the recirculating further comprises: selecting a first analytics module of the one or more analytics modules for iterations of training; providing the training data as input to the first analytics module for the calculated respective number of times for the first analytics module; and providing the training data to remaining one or ones of the one or more analytics modules once. 8. The method of claim 7, wherein the recirculating further comprises: selecting a second analytics module of the one or more analytics modules for iterations of training; providing the training data as input to the second analytics module for the calculated respective number of times for the second analytics module; and providing the training data to remaining one or ones of the one or more analytics modules once. 9. The method of claim 1, wherein the machine learning unit comprises a streaming analytics unit that uses an Adaptive Boosting (AdaBoost) algorithm. 10. The method of claim 1, further comprising: filtering out the labeled data from an output of the machine learning unit. 11. A non-transitory computer-readable storage medium having stored thereon computer-executable instructions executable by one or more computing devices to perform operations comprising: gathering, from a machine learning unit, a training data; identifying one or more analytics modules of the machine learning unit for training; and recirculating the training data through each of the one or more analytics modules a respective number of times to train the one or more analytics modules. 12. The computer-readable storage medium of claim 11, wherein the identifying comprises: evaluating outputs of a plurality of analytics modules including the one or more analytics modules of the machine learning unit; and selecting the one or more analytics modules from the plurality of analytics modules for training. 13. (canceled) 14. The computer-readable storage medium of claim 11, wherein the gathering comprises sifting classified data from the machine learning unit to select some of the classified data that is known to be valid as the training data. 15. The computer-readable storage medium of claim 12, wherein the selecting comprises selecting the one or more analytics modules for training as a result of an output of each of the one or more analytics modules being invalid. 16. The computer-readable storage medium of claim 12, wherein the evaluating comprises determining an output of a first analytics module of the one or more analytics modules to be valid following a recirculation of the training data through the first analytics module, and wherein the selecting comprises excluding the first analytics module from the one or more analytics modules for training. 17. (canceled) 18. (canceled) 19. (canceled) 20. (canceled) 21. The computer-readable storage medium of claim 11, further comprising: labeling the training data as labeled data; and filtering out the labeled data from an output of the machine learning unit. 22. An apparatus, comprising: a machine learning unit, comprising: a source module configured to provide a streaming input data, a plurality of analytics modules, each of the analytics modules coupled to receive and analyze the streaming input data to provide a respective data item and a respective classifier score, and a joint module coupled to collect the data items and classifier scores from the analytics modules to provide a stream of classified data; and an adaptive recirculation module coupled to the machine learning unit, the adaptive recirculation module configured to perform operations comprising: gathering, from the joint module, data as a training data, labeling the data to identify the data as the training data, and recirculating the labeled data for a respective number of times for each of a subset of the analytics modules. 23. The apparatus of claim 22, wherein the gathering comprises sifting the stream of classified data from the joint module to select some of the classified data that is known to be valid as the training data. 24. The apparatus of claim 22, wherein the recirculating comprises: evaluating outputs of the plurality of analytics modules; and identifying the subset of the analytics modules for training. 25. The apparatus of claim 24, wherein the identifying comprises identifying the subset of the analytics modules for training as a result of an output of each of the subset of the analytics modules being invalid. 26. The apparatus of claim 24, wherein the evaluating comprises determining an output of a first analytics module of the subset of the analytics modules to be valid following a recirculation of the training data through the first analytics module, and wherein the identifying comprises excluding the first analytics module from the subset of the analytics modules for training. 27. The apparatus of claim 24, wherein the recirculating further comprises: for each analytics module of the subset of the analytics modules, calculating a respective number of times that the training data is to be recirculated to the respective analytics module for training. 28. The apparatus of claim 27, wherein the recirculating further comprises: selecting a first analytics module of the subset of the analytics modules for iterations of training; providing the training data as input to the first analytics module for the calculated respective number of times for the first analytics module; and providing the training data to remaining one or ones of the subset of the analytics modules once. 29. The apparatus of claim 28, wherein the recirculating further comprises: selecting a second analytics module of the subset of the analytics modules for iterations of training; providing the training data as input to the second analytics module for the calculated respective number of times for the second analytics module; and providing the training data to remaining one or ones of the subset of the analytics modules once. 30. The apparatus of claim 22, wherein the machine learning unit comprises a streaming analytics unit that uses an Adaptive Boosting (AdaBoost) algorithm. 31. The apparatus of claim 22, wherein the adaptive recirculation module is configured to perform further operations comprising: filtering out the labeled data from the stream of classified data provided by the joint module.