April 2025
Intermediate
240 pages
6h 56m
Japanese
Content preview from AI開発力を鍛える！機械学習と最適化による問題解決講座
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3.5
ベイズ最適化：
可能な試行回数が少ない
ときは どうす れ ば よい か？
実際の最適化では実験コストが非常に高く、最適化のループを何度も回すこ
とができない場合があります。そのような状況で、機械学習モデルによる予
測を用いて、少しでも良い条件を探索する方法の1つがベイズ最適化です。
ベイズ最適化は、多様な最適化対象に用いられていますが、アルゴリズムの
中身は最適化の2 重構造となっていて複雑です。本節では、まず逐次最適化
について説明し、次いでベイズ最適化の中身を見ながら理解を深めます。最
後に、ベイズ最適化を用いたプロセス条件最適化の事例を紹介します。
1回だけの最適化と逐次最適化の違いとは？
これまでに、3.2節において機械学習試行を用いた最適化の特徴を学び、3.4節
において実際の事例（SiC結晶成長の装置構造とプロセス条件の最適化）を見ま
した。実際の試行を代替した機械学習試行を用いることで、最適化のループを高
速に回すことが可能となり、膨大な試行に基づき最適解を素早く取得することが
可能となりました。このとき、最適化は1回だけ行い、最適解を得たところで最
適化のフローチャートは終了でした。このことを
図 3.5.1 に模式的に示します。
先に取得したデータに対して、機械学習による回帰モデル（図では破線）を作成
し、この機械学習モデルに対して最適化アルゴリズムを作用させることで、最適
解（図では破線の最小位置）を求めます。この最適化に用いる機械学習モデルを
作成するためには、第
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.