計算力学講演会講演論文集
Online ISSN : 2424-2799
セッションID: 16-05
会議情報

ベイズ推論によるパターン認識
*一色 浩
著者情報
会議録・要旨集 認証あり

詳細
抄録

Deep learning has problems such as "inference is a black box", "unexpected answer by overfitting", and "large-scale network and long-time learning". Bayesian inference performs learning and inference that is completely different from neural networks There are two biggest differences between neural networks and Bayesian inference. The former is a data-dependent type and the latter is a deductive type. The other is the difference in degrees of freedom, the former is infinite degrees of freedom and the latter is limited degrees of freedom. Therefore, the former is superior in learning ability and the latter is superior in learning efficiency. In this paper, we discuss pattern recognition based on Bayesian inference using MNIST handwritten digit pattern data.

著者関連情報
© 2022 一般社団法人 日本機械学会
前の記事 次の記事
feedback
Top