Multilayer Perceptron Algebra

2017/01/18 by Peng Zhao, Peng, Zhao · 1 citation
Computer Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Medical Image Segmentation Techniques #Neural Networks and Applications #Rough Sets and Fuzzy Logic

paper · pdf · doi:10.48550/arxiv.1701.04968

openalex publication_date 2017/01/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Artificial Neural Networks(ANN) has been phenomenally successful on various pattern recognition tasks. However, the design of neural networks rely heavily on the experience and intuitions of individual developers. In this article, the author introduces a mathematical structure called MLP algebra on the set of all Multilayer Perceptron Neural Networks(MLP), which can serve as a guiding principle to build MLPs accommodating to the particular data sets, and to build complex MLPs from simpler ones.

Citations

• Big Neural Networks Waste Capacity
• Do Deep Convolutional Nets Really Need to be Deep and Convolutional?

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