Automated Machine Learning Hyperparameter Optimization
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Solution Overview
Problem
The challenge in machine learning is selecting appropriate neural networks and hyperparameters that match the characteristics of input data, especially in industries where inference time is critical, and non-professionals face difficulties in determining optimal hyperparameter settings through exhaustive experiments.
Innovation Solution
A method and apparatus for automated machine learning that registers and optimizes network functions and parameters by combining different parameter sets, performing cross-validation, and selecting the best sets based on learning conditions and input datasets to produce and validate learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural networks are used to improve learning performance, then prediction and detection accuracy is improved, but inference time increases and becomes unsuitable for industrial applications
Solution Approach 1:
The system automatically adjusts network architecture parameters (depth, width, layer types) and hyperparameters (learning rate, batch size, optimizer settings) based on the specific characteristics of the input data and task requirements. This allows selecting optimal network depth - shallow for real-time industrial applications, deeper for accuracy-critical applications - rather than always using deep networks.
Solution Approach 2:
The patent implements dynamic model selection where the system adapts the network architecture and parameters based on the specific dataset characteristics, task type, and performance requirements. The automated machine learning process dynamically determines the appropriate model complexity for each specific application scenario.
2Measurement precision
If exhaustive experiments are conducted to find optimal hyperparameters, then learning model performance is improved, but the time and computational resources required increase significantly
Solution Approach 1:
The system performs preliminary analysis of the input data characteristics (distribution, size, type, relationships) before conducting hyperparameter optimization. This preliminary action allows the automated machine learning system to narrow down the search space and select more promising hyperparameter ranges, reducing the overall optimization time while maintaining effectiveness.
Solution Approach 2:
The patent implements feedback mechanisms where the system learns from validation results and adjusts subsequent hyperparameter search strategies. By using validation scores and performance metrics from preliminary experiments, the system refines its hyperparameter selection process, avoiding exhaustive search while converging to optimal settings more efficiently.
3Measurement precision
If the user manually selects neural networks and hyperparameters, then the process requires professional expertise and extensive knowledge, but non-professionals cannot effectively perform this task
Solution Approach 1:
The system performs automatic model selection and hyperparameter optimization without requiring user expertise in machine learning. The automated machine learning platform analyzes the data, selects appropriate network architectures, and optimizes parameters autonomously, making professional-grade model development accessible to non-experts through a user-friendly interface.
Solution Approach 2:
The patent introduces an automated machine learning system as an intermediary between the user and the complex process of model selection. This intermediary handles the expertise-intensive tasks of network architecture selection and hyperparameter tuning, translating user requirements into optimized models without requiring the user to possess specialized knowledge.
4Adaptability or versatility
If there is no standardized method for selecting neural networks, then flexibility to adapt to different fields and data characteristics is maintained, but the complexity of the selection process increases
Solution Approach 1:
The patent implements a universal automated machine learning framework that handles diverse data types, task categories, and application domains through a single integrated system. The platform automatically adapts to different fields and data characteristics using standardized processes for data analysis, model selection, and optimization, reducing selection complexity while maintaining broad adaptability.
Data Source
AI summary
Provided is a method and apparatus for automated machine learning, and the method for automated machine learning includes: registering at least one or more first parameter sets including combinations of different set data for at least one or more parameters having an influence on the performance of learning models; choosing at least one or more second parameter sets to be used for the production of the learning models from the first parameter sets, based on learning conditions inputted; performing learning for network functions, based on the chosen second parameter sets and given input datasets to produce the learning models corresponding to the second parameter sets and calculating validation scores for the respective learning models produced; and choosing one of the produced learning models as an application model, based on the validation scores.