Weighted Example Selection for Machine Learning Model Training
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Solution Overview
Problem
Current systems for training machine learning algorithms lack efficient methods for selective use of examples, dataset management, and resource optimization, leading to suboptimal performance and resource utilization.
Innovation Solution
The system allows for selective use of examples by assigning weights based on external device information, generating and updating inference models, and employing them based on available processing resources, while also enabling personalized quality assurance and incremental annotation of datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all examples are used for training machine learning algorithms, then the model may achieve better generalization, but the training time and computational resources increase significantly
Solution Approach 1:
The patent changes the parameter of example selection from binary (include/exclude) to continuous weighting, where each example is assigned a weight value that reflects its importance. This allows the system to optimize between model performance and training efficiency by adjusting weights rather than simply selecting or rejecting examples
Solution Approach 2:
The patent applies local quality by assigning different weights to different examples based on their specific characteristics and relevance to the task. Instead of uniform treatment of all examples, each example receives a tailored weight that optimizes its contribution to model training
2Measurement precision
If more processing resources are allocated to training, then model accuracy improves, but system resource utilization becomes inefficient
Solution Approach 1:
The patent implements partial action by selectively emphasizing only the most relevant examples through higher weights, rather than processing all examples with equal intensity. This partial focus on critical examples achieves high model accuracy while avoiding the resource expenditure of uniformly processing all training data
3Stability of the object's composition
If the system processes all incoming data changes, then data consistency is maintained, but processing speed and system responsiveness decrease
Solution Approach 1:
The patent transforms the data processing approach from binary (process/all or ignore) to weighted prioritization. By assigning weights to data changes based on their importance, the system can maintain consistency for critical data while allowing less important changes to be processed asynchronously or with lower priority
Data Source
AI summary
Systems and methods for selective use of examples are provided. For example, information associated with external devices may be obtained, weights may be assigned to examples using the obtained information, and inference models may be generated using the assignment of weights. In some examples, update information associated with the external devices may be obtained, the assignment of weights may be updated using the update information, and updated inference models may be generated using the updated assignment of weights.