主催: バイオメディカル・ファジィ・システム学会
会議名: 第29回バイオメディカル・ファジィ・システム学会
回次: 29
開催地: 高知
開催日: 2016/11/26 - 2016/11/27
p. 157-160
AutoEncoder, which acquires a specific feature space model from unsupervised data, has come to be one of the key technologies for designing a system based on neural networks. In this paper, we conduct an assessment of three types of constraint for AutoEncoder. As results of two experiments, we confirmed the sparse coding constraints is valuable for applying the acquired feature space to noisy photographed document analysis.