ROBUST PERSONALIZATION THROUGH BIASED REGULARIZATION
First Claim
1. A system for enhancing performance of a data pattern recognizer for an entity, comprising:
- a personalization component that updates the recognizer based at least in part upon at least one sample customized to the entity, the personalization component utilizes regularization favoring a base set of parameters of the recognizer during update of the recognizer.
2 Assignments
0 Petitions
Accused Products
Abstract
The subject disclosure pertains to systems and methods for personalization of a recognizer. In general, recognizers can be used to classify input data. During personalization, a recognizer is provided with samples specific to a user, entity or format to improve performance for the specific user, entity or format. Biased regularization can be utilized during personalization to maintain recognizer performance for non-user specific input. In one aspect, regularization can be biased to the original parameters of the recognizer, such that the recognizer is not modified excessively during personalization.
-
Citations
20 Claims
-
1. A system for enhancing performance of a data pattern recognizer for an entity, comprising:
a personalization component that updates the recognizer based at least in part upon at least one sample customized to the entity, the personalization component utilizes regularization favoring a base set of parameters of the recognizer during update of the recognizer. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 15, 16)
-
10. A method for enhancing the performance of a trained recognizer for an individual, comprising:
retraining the trained recognizer based at least in part upon at least one sample personalized to the individual using regularization biased to a base state of the trained recognizer. - View Dependent Claims (11, 12, 13, 14)
-
17. A system for enhancing data classification for a recognizer for an individual, comprising:
means for training the recognizer based at least in part upon at least one sample generated by the individual using regularization biased to a base weights of the recognizer. - View Dependent Claims (18, 19, 20)
Specification