In the era of big data, it is aimed to use big data technology to form an effective early warning
and prevention of Internet credit. The BP neural network algorithm is applied to determine …

Abstract Deep Nonnegative Matrix Factorization (Deep NMF) is an effective strategy for
feature extraction in recent years. By decomposing the matrix recurrently on account of the …

The non-negative matrix factorization (NMF) algorithm represents the original image as a
linear combination of a set of basis images. This image representation method is in line with …

This study is to explore the optimization of the adaptive genetic algorithm (AGA) in the
backpropagation (BP) neural network (BPNN), so as to expand the application of the BPNN …

The financial risks of commercial banks are classified and evaluated through the Internet of
Things (IoT) technology and big data technology to reduce the financial risk loss of …

Because of the large structure and long training time, the development cycle of the common
depth model is prolonged. How to speed up training is a problem deserving of study. In …

Backpropagation (BP) has been widely used as a de-facto standard algorithm to compute
weights for deep neural networks (DNNs). The BP method is based on a stochastic gradient …

In this paper, we focus on deep semi-nonnegative matrix factorization (DSemiNMF) which
has a wider application in the real world than traditional NMF. We propose an efficient …

Backpropagation (BP) is the most widely used algorithm for the training of deep neural
networks (DNN) and is also considered a de facto standard algorithm. However, the BP …

行列分解を基盤としたディープニ ューラルネットワーク計算法 (諸科 学分野を結ぶ基礎学問と Page 1
RIGHT: URL: CITATION: AUTHOR(S): ISSUE DATE: TITLE: 行列分解を基盤としたディープニ ュー …