  The simulation of biological dendrite computations is vital for the
development of artificial intelligence (AI). 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$\backslash$or$\backslash$not). Experiments and main results: DD, a
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 MNIST and FASHION-MNIST 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. The main contribution of
this paper is the basic machine learning algorithm (DD) with a 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 GitHub:
https://github.com/liugang1234567/Gang-neuron .
