Research of the DBN Algorithm Based on Multi-innovation Theory and Application of Social Computing

• 作者: Chuanpeng Li Meng Li Pinle Qin Qiguang Miao
• 来源期刊: 国际计算机前沿大会会议论文集
• 学科: 社会科学
• 关键词: DBN ALGORITHM CONVERGENCE error Multi-innovation THEORY MI-DBN ALGORITHM Social COMPUTING
• 年，卷（期）: 2016,（1）
• 页码范围: 147-149
• 页数: 3页
• 分类号: C5

摘要

Aimed at the problems of small gradient, low learning rate, slow convergence error when the DBN using back-propagation process to fix the network connection weight and bias, proposing a new algorithm that combines with multi-innovation theory to improve standard DBN algorithm, that is the multi-innovation DBN(MI-DBN). It sets up a new model of back-propagation process in DBN algorithm, making the use of single innovation in previous algorithm extend to the use of innovation of the preceding multiple period, thus increasing convergence rate of error largely. To study the application of the algorithm in the social computing, and recognize the meaningful information about the handwritten numbers in social networking images. This paper compares MI-DBN algorithm with other representative classifiers through experiments. The result shows that MI-DBN algorithm, comparing with other representative classifiers, has a faster convergence rate and a smaller error for MNIST dataset recognition. And handwritten numbers on the image also have a precise degree of recognition.