Accelerated Boosting Classifier Training via Operator Relevance
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Solution Overview
Problem
The existing boosting techniques for training classifiers are slow, limiting their applicability due to the time-consuming training process, especially when dealing with large sets of images for tasks like image classification.
Innovation Solution
An accelerated boosting process that selectively tests a restricted set of linear operators using relevance estimates to identify error-minimizing operators, focusing on re-weighting the training set to prioritize misclassified images, and iteratively training classifiers with a weighted version of the training set, reducing the computational burden by testing only the top-ranked operators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If boosting is used to train classifiers, then classification accuracy is improved, but training time increases significantly
Solution Approach 1:
The patent computes relevance estimates for all operators before the actual training process begins. This preliminary ranking allows the training algorithm to focus only on the most relevant operators during each boosting iteration, significantly reducing the computational burden while maintaining the ability to achieve high classification accuracy through selective operator evaluation.
Solution Approach 2:
The patent segments the operator set into different relevance tiers based on pre-computed relevance estimates. Instead of evaluating all operators uniformly, the system divides them into high-relevance, medium-relevance, and low-relevance groups, allowing the boosting process to efficiently select from the most promising segments while ignoring less relevant operators.
2Reliability
If a large set of linear operators is tested to find the error-minimizing operator, then classifier accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent performs a preliminary computation of relevance estimates for all operators before the main training process. This upfront work creates a ranking that guides subsequent operator selection, allowing the system to achieve high classifier accuracy by focusing computational resources on the most relevant operators rather than uniformly testing all operators.
Solution Approach 2:
The patent changes the parameter of operator evaluation from exhaustive testing to relevance-based selective testing. By introducing relevance estimates as a filtering parameter, the system transforms the computational approach from testing all operators to testing only those above a certain relevance threshold, significantly reducing computational complexity while maintaining accuracy.
3Measurement precision
If all linear operators are tested against the training set, then the best operator is identified, but processing time increases
Solution Approach 1:
The patent computes relevance estimates for all operators before the actual training process begins. This preliminary ranking allows the training algorithm to focus only on the most relevant operators during each boosting iteration, significantly reducing the computational burden while maintaining the ability to achieve high classification accuracy through selective operator evaluation.
Solution Approach 2:
The patent applies partial action by testing only a subset of operators (those with highest relevance estimates) rather than all operators in the set. This selective approach achieves sufficient operator selection accuracy for effective training while dramatically improving processing speed by avoiding evaluation of less relevant operators.
Data Source
AI summary
Systems, methods, and computer program products implementing techniques for training classifiers. The techniques include receiving a training set that includes positive images and negative images, receiving a restricted set of linear operators, and using a boosting process to train a classifier to discriminate between the positive and negative images. The boosting process is an iterative process. The iterations include a first iteration where a classifier is trained by (1) testing some, but not all linear operators in the restricted set against a weighted version of the training set, (2) selecting for use by the classifier the linear operator with the lowest error rate, and (3) generating a re-weighted version of the training set. The iterations also include subsequent iterations during which another classifier is trained by repeating steps (1), (2), and (3), but using in step (1) the re-weighted version of the training set generated during a previous iteration.