Abstract
This paper presents a basic machine learning algorithm, named Dendrite Net or
DD, just like Support Vector Machine (SVM) or Multilayer Perceptron (MLP). DD's
main concept is that the algorithm can recognize this class after learning, if
the output's logical expression contains the corresponding class's logical
relationship among inputs ($ and or not $). Experiments
and results: DD, the first white-box machine learning algorithm, showed
excellent system identification performance for the black-box system. Secondly,
it was verified by nine real-world applications that DD brought better
generalization capability relative to MLP architecture that imitated neurons'
cell body (Cell body Net) for regression. Thirdly, by MINIST and FASHION-MINIST
datasets, it was verified that DD showed higher testing accuracy under greater
training loss than Cell body Net for classification. The number of modules can
effectively adjust DD's logical expression capacity, which avoids over-fitting
and makes it easy to get a model with outstanding generalization capability.
Finally, repeated experiments in $ MATLAB $ and $ PyTorch $ ($ Python $)
demonstrated that DD was faster than Cell body Net both in epoch and
forward-propagation. We highlight DD's white-box attribute, controllable
precision for better generalization capability, and lower computational
complexity. Not only can DD be used for generalized engineering, but DD has
vast development potential as a module for deep learning. DD code is available
at https://github.com/liugang1234567/Gang-neuron.
Description
Dendrite Net: A White-Box Module for Classification, Regression, and System Identification
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