[Data] Bayesian Optimization
This is an introduction to a method for exploratory determination of the maximum and minimum values of functions with unknown shapes or functions that cannot be differentiated.
Bayesian optimization is a method for exploratory search to find the maximum or minimum of a function with an unknown overall shape or a function that cannot be differentiated, while estimating the shape of the function. The key point is how to find an optimal value with as few evaluations as possible in situations where unknown evaluations are very time-consuming and costly. In this document, we also include "examples of applications in the FA field," "system integration and wide-ranging proposals," and "an overview of the Pipeline Pilot product." [Examples of Bayesian Optimization Applications] - Optimization of operating conditions for various devices - Exploration of optimal compositions for various materials - Utilization in hyperparameter search for machine learning - Efficient discovery of high-accuracy learning models *For more details, please refer to the PDF document or feel free to contact us.
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