Multi-stage Machine Learning System with Learning Coach
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Solution Overview
Problem
Existing machine learning systems face inefficiencies in processing large datasets, as the computational power required grows exponentially with the amount of data, leading to performance issues in applications like image and speech recognition.
Innovation Solution
A multi-stage machine learning system is introduced, where data is passed through multiple stages of machine learning systems, with a learning coach and data management system optimizing the distribution and hyperparameters to select only a small fraction of final stage systems for data processing, reducing computational load and improving efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all machine learning systems in the final stage process every data item, then classification accuracy is improved, but computational power requirements increase exponentially
Solution Approach 1:
The patent segments the monolithic machine learning system into multiple stages: initial machine learning systems that perform preliminary classification, and final stage machine learning systems that perform detailed classification. This segmentation allows data to be processed differently at each stage, with only relevant data reaching the computationally intensive final stage systems, thereby reducing overall computational power requirements while maintaining classification accuracy.
Solution Approach 2:
The patent introduces intermediate machine learning systems between the input data and the final stage systems. These intermediate systems act as mediators that filter and route data based on preliminary analysis, ensuring that only data requiring detailed classification reaches the final stage systems. This intermediary layer significantly reduces the computational burden on final stage systems while preserving accuracy for relevant cases.
2Reliability
If more final stage machine learning systems are used, then robustness is improved, but computational efficiency deteriorates
Solution Approach 1:
The patent applies local quality by having different machine learning systems specialize in different aspects of classification. Each final stage system is optimized for specific types of data or classification tasks, rather than all systems processing all data equally. This specialization maintains robustness through diverse expertise while improving computational efficiency by routing data to the most appropriate specialized system.
Solution Approach 2:
The patent implements partial action by having intermediate systems handle the bulk of preliminary processing and filtering tasks. The final stage systems then perform only the necessary detailed classification on filtered data, rather than all systems performing full classification on all data. This partial division of labor maintains robustness through multiple specialized systems while dramatically improving computational efficiency.
3Reliability
If data is distributed to all final stage systems, then model robustness is improved, but training and operational cost increase
Solution Approach 1:
The patent applies preliminary action by having intermediate machine learning systems perform initial classification and filtering before data reaches the final stage systems. This preliminary processing identifies which data items require detailed classification and routes them accordingly. As a result, data is distributed to final stage systems selectively based on need rather than universally, maintaining model robustness through diverse processing while reducing computational cost by avoiding unnecessary processing of irrelevant data.
Data Source
AI summary
A multi-stage machine learning and recognition system comprises multiple individual machine learning systems arranged in multiple stages, where data is passed from a machine learning system in one stage to one or more machine learning systems in a subsequent, higher-level stage of the structure according to the logic of the machine learning system. The multi-stage machine learning system can be arranged in a final stage and one or more non-final stages, where the one or more non-final stages direct data generally towards a selected one or more machine learning systems within the final stage, but less than all of the machine learning systems in the final stage. The multi-stage machine learning system can additionally include a learning coach and data management system, which is configured to control the distribution of data throughout the multi-stage structure of machine learning systems by observing the internal state of the structure.