# How do data mining classifiers behave when we add training samples to previously established training models?

I have yet to choose a classifier to get my model but I wanted to see which possible classifiers fit what I'm looking for. In fact, I need an algorithm to establish offline training this way:

• The first training starts after a month of collecting data (I know it's a short time but I want to see how it performs and how it gets better with time and more data) => first model is made
• After another month, I've got data collected after two months in total, so I want to establish a new model since my training data has just doubled and the model should provide better results => second model
• this continues on and on for a year...

Question1: After two months, when I want to add the new training data, would the previous model be updated to make the new model or would the training start all over as if I don't have a previous model?

Question2: Do all classifiers behave the same when new training samples are introduced?

I don't know if this is relevant, but my problem is a low-dimensional one: about 5 inputs (user location, date, time...) to predict one output.

The answer to question 2 is no. As a simplistic (and hand wavy) example, consider the case where you observe a sequence of observations: $$(y_1, X_1),...(y_n,X_n) \in \mathbb{R^p}\times\mathbb{R},$$ and that you are interested in estimating a linear regression: $$y_i = X_i\beta + \varepsilon_i.$$ One possibility is to do so via the usual least squares estimator: $$\hat\beta = (X^{T}X)^{-1}X^{T}y.$$
Another option, in case outliers are of concern, is to use a robust estimating equation: $$\hat\beta = \arg\max_\beta \varphi(X\beta - y),$$ for more on robust regression see- Robust regression. Now, suppose that the $$n+1$$ observation is an outlier, so for example, $$y_{n+1} = 10^5 * \max_{i\in 1:n} |x_i|.$$