Let me explain this with a simple example: predicting housing price using features such as
number of bedrooms,
number of bathrooms, etc.
Now that the market is changing every day, there's an 'underlying rule' that the house price is closely related to
a * number of bedrooms + b. I know this rule and I want to incorporate this to my model. The parameters
b are changing every day, but not changing within the day. My plan is to add an additional input feature, which is
a * number of bedrooms + b, and train
b together with other features.
Assume we have sufficient data, I want to develop a model that
train on housing prices every morning, predict some prices in the afternoon.
learn the parameters
bwhile training, use them in predicting, then re-train them the other day. i.e. reset
bat the end of each day.
for other features, I want to preserve the weights and train them further every morning.
Can I do this without using a separate linear regressor?