  The power of big data and machine learning has been drastically demonstrated
in many fields during the past twenty years which somehow leads to the vague
even false understanding that the huge amount of precious human knowledge
accumulated to date no longer seems to matter. In this paper, we are pioneering
to propose the knowledge-driven machine learning(KDML) model to exhibit that
knowledge can play an important role in machine learning tasks. KDML takes
advantage of domain knowledge to processes the input data by space transforming
without any training which enable the space of input and the output data of the
neural networks to be identical, so that we can simplify the machine learning
network structure and reduce training costs significantly. Channel estimation
problems considering the time selective and frequency selective fading in
wireless communications are taken as a case study, where we choose least
square(LS) and minimum meansquare error(MMSE) as knowledge module and Long
Short Term Memory(LSTM) as learning module. The performance obtained by KDML
channel estimator obviously outperforms that of knowledge processing or
conventional machine learning, respectively. Our work sheds light on the new
area of machine learning and knowledge processing.
