[인용][C] 비지도 딥러닝 및 자기조직 신경망을 이용한 영상 분류 알고리즘 개발
이종혁， 권기훈， 윤종필， 김민영 - 제어로봇시스템학회 국내학술대회 …, 2018 - dbpia.co.kr
이종혁， 권기훈， 윤종필， 김민영
제어로봇시스템학회 국내학술대회 논문집, 2018•dbpia.co.krIn recently, supervised learning is one of the most used artificial intelligence systems in the
factory. But It takes a lot of time and manpower to classify each type of defect. In this paper,
propose a clustering method using feature combining Autoencoder and Self-Organizing
Map techniques. The experimental results show 60% clustering accuracy for MNIST dataset
and 90% accuracy for fashion MNIST dataset.
factory. But It takes a lot of time and manpower to classify each type of defect. In this paper,
propose a clustering method using feature combining Autoencoder and Self-Organizing
Map techniques. The experimental results show 60% clustering accuracy for MNIST dataset
and 90% accuracy for fashion MNIST dataset.
Abstract
In recently, supervised learning is one of the most used artificial intelligence systems in the factory. But It takes a lot of time and manpower to classify each type of defect. In this paper, propose a clustering method using feature combining Autoencoder and Self-Organizing Map techniques. The experimental results show 60% clustering accuracy for MNIST dataset and 90% accuracy for fashion MNIST dataset.
dbpia.co.kr
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