Weight Benefit Evaluator for Machine Learning Training Data
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Solution Overview
Problem
In machine learning environments, determining the optimal application of weights to training data to enhance learning functions is challenging, as it can lead to sample loss and unintended performance degradation, necessitating a method to evaluate the weight benefit effectively.
Innovation Solution
A system and method that determine a weight benefit by generating weighted training and target data, comparing functions based on these data sets, and evaluating the benefit of applying weights to training data, using a device with modules for processing, machine learning, and evaluation to assess the impact on learned functions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If weights are applied to training data to enhance learning functions, then learning performance may be improved, but sample loss and performance degradation may occur
Solution Approach 1:
The patent applies preliminary action by generating synthetic target data and computing weight benefits before actually applying weights to the training data. The system evaluates the expected impact of weighting through function comparisons (first function from training data, second function from weighted training data, third function from target data, fourth function from weighted target data) and determines weight benefits in advance, allowing optimization of weighting parameters without directly applying them to the original training samples, thereby preventing sample loss while maintaining performance improvement potential
Solution Approach 2:
The patent introduces an intermediary evaluation mechanism that acts as a mediator between the training data and the weighting process. The synthetic target data and the function comparison process serve as intermediaries to assess weight benefits indirectly. Instead of directly applying weights to training data (which causes sample loss), the system uses these intermediary functions and evaluations to determine optimal weighting strategies, thus protecting the original training samples while still enabling performance enhancement through computed weight benefits
2Reliability
If weights are applied to training data, then learning function effectiveness may be enhanced, but unintended performance degradation may occur
Solution Approach 1:
The patent implements feedback by continuously evaluating the impact of weighting through function comparisons. The system computes the first function based on training data, the second function based on weighted training data, the third function based on target data, and the fourth function based on weighted target data. By comparing these functions and determining weight benefits, the system provides feedback on whether weighting is having the desired effect or causing degradation, allowing for dynamic adjustment of weighting parameters to prevent harmful performance degradation while maintaining effectiveness enhancement
Solution Approach 2:
The system performs preliminary evaluation of weight benefits before applying weights to prevent unintended performance degradation. By computing functions and evaluating weight benefits in advance using synthetic target data, the system can identify potential harmful effects before they manifest in the actual learning process, allowing for corrective adjustments to be made proactively rather than reactively
Data Source
AI summary
Technologies are generally described for methods and systems effective to determine a weight benefit associated with application of weights to training data in a machine learning environment. In an example, a device may determine a first function based on the training data, where the training data includes training inputs and training labels. The device may determine a second function based on weighted training data, which is based on application of weights to the training data. The device may determine a third function based on target data, where the target data is generated based on a target function. The target data may include target labels different from the training labels. The device may determine a fourth function based on weighted target data, which is a result of application of weights to the target data. The device may determine the weight benefit based on the first, second, third, and fourth functions.