Statistical Model Training Data Selection
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Solution Overview
Problem
Conventional training processes for statistical models, such as machine learning models, require large datasets, leading to significant resource and time costs, as they treat all data instances equally without distinguishing between easy-to-classify and hard-to-classify samples, resulting in inefficient resource allocation.
Innovation Solution
Implementing a dynamic and intelligent selection of training data subsets for each iteration, focusing on harder-to-classify samples while reducing the processing of easy samples, using techniques like sampling and cutoff thresholds to optimize resource usage and accelerate training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large datasets with many instances of classified data are used to train statistical models with high accuracy, then model accuracy is improved, but resource cost and training time increase significantly
Solution Approach 1:
The patent segments the training data into different subsets based on classification difficulty. Easy-to-classify samples and hard-to-classify samples are separated into different training batches, allowing the model to process them differently. This segmentation enables efficient resource allocation where easy samples can be processed faster or with less computational power, while hard samples receive more focused processing attention.
Solution Approach 2:
The patent applies local quality by assigning different processing qualities or computational resources to different parts of the dataset based on their classification difficulty. Easy samples are handled with standard or reduced processing, while hard samples receive enhanced processing resources. This localized quality adjustment optimizes the overall training efficiency without compromising model accuracy on challenging cases.
2Measurement precision
If large datasets with many instances of classified data are used to train statistical models with high accuracy, then model accuracy is improved, but resource cost increases significantly
Solution Approach 1:
The training dataset is divided into segments based on classification difficulty, allowing the system to allocate computational resources more efficiently. Easy-to-classify samples are processed with fewer computational resources, while hard-to-classify samples receive more intensive processing. This segmentation reduces the total computational energy required while maintaining model accuracy.
Solution Approach 2:
The patent changes processing parameters dynamically based on sample difficulty. Different computational parameters (such as processing depth, model complexity, or iteration count) are applied to easy versus hard samples. This parameter adaptation reduces overall computational resource consumption while ensuring that critical hard samples receive sufficient processing power to maintain model accuracy.
3Reliability
If all data instances are processed equally in conventional training processes, then comprehensive training coverage is achieved, but processing efficiency decreases due to unnecessary processing of easy samples
Solution Approach 1:
The patent segments the dataset into easy-to-classify and hard-to-classify subsets, allowing differentiated processing strategies. This segmentation maintains comprehensive training coverage by ensuring both subsets are represented in training, while improving productivity by applying efficient processing to easy samples and focused processing to hard samples.
Solution Approach 2:
The patent applies partial action by processing different subsets of data with different levels of intensity. Easy samples receive minimal or standard processing (partial action), while hard samples receive more intensive processing. This approach maintains sufficient training coverage without the excessive processing of all samples at maximum intensity, thereby improving overall processing efficiency.
Data Source
AI summary
Subsets of training data are selected for iterations of a statistical model through a training process. The selection can reduce the amount of data to be processed by selecting the training data that will likely have significant training value for the pass. This can include using a metric such as the loss or certainty to sample the data, such that easy to classify instances are used for training less frequently than harder to classify instances. A cutoff value or threshold can also, or alternatively, be used such that harder to classify instances are not selected for training until later in the process when the model may be more likely to benefit from training on those instances. Sampling can vary between passes for variety, and the cutoff value might also change such that all data instances are eligible for training selection by at least the last iteration.