Biased Regularization for Personalized Recognizer Adaptation
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Solution Overview
Problem
Machine learning systems face challenges in pattern recognition and classification, particularly when dealing with variations in data provided by different entities, such as handwriting styles or voice accents, leading to poor performance in recognizing input from new users or entities with rare writing styles or dialects.
Innovation Solution
The personalization of recognizers using biased regularization, where the base parameters of a generic recognizer are updated with customized samples from a specific user or entity, while limiting changes to maintain performance on non-user specific input data, thereby optimizing performance for the individual while minimizing deviation from the base parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the base parameters of the recognizer are updated using customized samples from a specific user, then the recognizer's performance for that specific user is improved, but the performance for non-user specific input data deteriorates
Solution Approach 1:
The patent applies parameter changes by modifying the base parameters of the recognizer using customized samples from specific users. The system updates the parameters through biased regularization, which adjusts the parameters to improve performance for the specific user while constraining the magnitude of changes to maintain general performance. This is achieved by minimizing a regularized error function that balances fitting the customized samples and maintaining proximity to the base parameters.
Solution Approach 2:
The patent implements partial action by applying biased regularization that only partially updates the base parameters based on customized samples. Instead of completely retraining the recognizer on user-specific data, the system performs a controlled, partial update that captures user-specific patterns while retaining the general knowledge from the base recognizer. This partial update approach prevents over-specialization and maintains robustness for non-user specific input.
2Measurement precision
If the parameters of the recognizer are modified dramatically to improve performance on customized samples, then the error rate on customized samples decreases, but the deviation from base parameters increases excessively
Solution Approach 1:
The patent applies beforehand cushioning by using biased regularization to pre-constrain the parameter updates before they occur. The regularization term acts as a cushion that prevents excessive deviation from the base parameters during the update process. By incorporating this constraint into the optimization objective, the system ensures that parameter changes remain within acceptable bounds, preventing overfitting to the customized samples while still improving performance.
3Measurement precision
If more customized samples are used for training, then the recognizer performance for specific users improves, but the training complexity and time increase
Solution Approach 1:
The patent extracts only the essential user-specific patterns from the customized samples through biased regularization, rather than requiring extensive training on large numbers of samples. The method extracts the most important deviations from the base parameters that capture user-specific characteristics, achieving good performance with fewer samples. This extraction approach reduces the computational burden and training time while maintaining effectiveness.
Data Source
AI summary
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.