Systems and methods may provide for using one or more generic classifiers to generate self-training data based on a first plurality of events associated with a device, and training a personal classifier based on the self-training data. Additionally, the one or more generic classifiers and the personal classifier to may be used to generate validation data based on a second plurality of events associated with the device. In one example, the personal classifier is substituted for the one or more generic classifiers if the validation data indicates that the personal classifier satisfies a confidence condition relative to the one or more generic classifiers.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A wearable device comprising: a battery to power the wearable device; a user interface to detect a first plurality of events during a self-training stage of the wearable device and a second plurality of events during a validation stage of the wearable device; one or more generic classifiers to generate self-training data based on the first plurality of events; a personal classifier, wherein the one or more generic classifiers and the personal classifier are to generate validation data based on the second plurality of events; and a validator coupled to the one or more generic classifiers and the personal classifier, the validator to substitute the personal classifier for the one or more generic classifiers if the validation data indicates that the personal classifier satisfies a confidence condition relative to the one or more generic classifiers. 2. The wearable device of claim 1, wherein each of the one or more generic classifiers is trained offline for a plurality of individuals and the personal classifier is to be trained online for a single individual. 3. The wearable device of claim 1, wherein the confidence condition is to include an agreement threshold between the one or more generic classifiers and the personal classifier being reached. 4. The wearable device of claim 3, wherein the confidence condition is to further include a validation instance threshold being reached. 5. The wearable device of claim 1, further including a trainer coupled to the personal classifier, the trainer to train the personal classifier based on the self-training data. 6. The wearable device of claim 5, further including a confidence verifier to conduct a periodic verification that the confidence condition is satisfied, wherein the trainer is to re-train the personal classifier if the periodic verification is unsuccessful. 7. The wearable device of claim 5, further including a hardware accelerator, wherein the trainer is to map the personal classifier to the hardware accelerator. 8. A method of operating a device, comprising: using one or more generic classifiers to generate self-training data based on a first plurality of events associated with the device; using the one or more generic classifiers and a personal classifier to generate validation data based a second plurality of events associated with the device; and substituting the personal classifier for the one or more generic classifiers if the validation data indicates that the personal classifier satisfies a confidence condition relative to the one or more generic classifiers. 9. The method of claim 8, wherein each of the one or more generic classifiers is trained offline for a plurality of individuals and the personal classifier is trained online for a single individual. 10. The method of claim 8, wherein the confidence condition includes an agreement threshold between the one or more generic classifiers and the personal classifier being reached. 11. The method of claim 10, wherein the confidence condition further includes a validation instance threshold being reached. 12. The method of claim 8, further including: training the personal classifier based on the self-training data; and mapping the personal classifier to a hardware accelerator. 13. The method of claim 12, further including: conducting a periodic verification that the confidence condition is satisfied; and re-training the personal classifier if the periodic verification is unsuccessful. 14. At least one computer readable storage medium comprising a set of instructions which, when executed by a computing device, cause the computing device to: use one or more generic classifiers to generate self-training data based on a first plurality of events associated with the computing device; use the one or more generic classifiers and a personal classifier to generate validation data based on a second plurality of events associated with the device; and substitute the personal classifier for the one or more generic classifiers if the validation data indicates that the personal classifier satisfies a confidence condition relative to the one or more generic classifiers. 15. The at least one computer readable storage medium of claim 14, wherein each of the one or more generic classifiers is to be trained offline for a plurality of individuals and the personal classifier is to be trained online for a single individual. 16. The at least one computer readable storage medium of claim 14, wherein the confidence condition is to include an agreement threshold between the one or more generic classifiers and the personal classifier being reached. 17. The at least one computer readable storage medium of claim 16, wherein the confidence condition is to further include a validation instance threshold being reached. 18. The at least one computer readable storage medium of claim 14, wherein the instructions, when executed, cause the computing device to: train the personal classifier based on the self-training data; and map the personal classifier to a hardware accelerator. 19. The at least one computer readable storage medium of claim 17, wherein the instructions, when executed, cause the computing device to: conduct a periodic verification that the confidence condition is satisfied; and re-train the personal classifier if the periodic verification is unsuccessful. 20. An event categorization apparatus comprising: one or more generic classifiers to generate self-training data based on a first plurality of events associated with a device; a personal classifier, wherein the one or more generic classifiers and the personal classifier are to generate validation data based on a second plurality of events associated with the device; and a validator coupled to the one or more generic classifiers and the personal classifier, the validator to substitute the personal classifier for the one or more generic classifiers if the validation data indicates that the personal classifier satisfies a confidence condition relative to the one or more generic classifiers. 21. The apparatus of claim 20, wherein each of the one or more generic classifiers is trained offline for a plurality of individuals and the personal classifier is to be trained online for a single individual. 22. The apparatus of claim 20, wherein the confidence condition is to include an agreement threshold between the one or more generic classifiers and the personal classifier being reached. 23. The apparatus of claim 22, wherein the confidence condition is to further include a validation instance threshold being reached. 24. The apparatus of claim 20, further including a trainer coupled to the personal classifier, the trainer to train the personal classifier based on the self-training data and map the personal classifier to a hardware accelerator. 25. The apparatus of claim 24, further including a confidence verifier to conduct a periodic verification that the confidence condition is satisfied, wherein the trainer is to re-train the personal classifier if the periodic verification is unsuccessful.