Is there a difference between on-line learning, incremental learning and sequential learning? What I mean is the following: Instead of processing all the training data at once and calculating a model, we process one data point at a time and update the model directly afterwards. 

I have seen the terms "on-line (or online) learning" and "incremental learning" for this. Is there a subtle difference? Is one term used more frequently? Or does it depend on the research community?

Edit: The Bishop book (Pattern Recognition and Machine Learning) uses the terms on-line learning and sequential learning as synonyms but does not mention incremental learning.
So, the following answer is just based on different opinions of collegues and professors from the field. I want to try to summarize it briefly:

Sequential and online learning is mostly associated with Bayesian updating. The sequential learning is used widely for an order in time of the data, meaning that $x_1$ is coming always first, then $x_2$, then $x_3$ and so on. The Dataset each has a certain order in that sense. 
In contrast to that incremental may be a whole block of data at time x and another block of data at time y. While the block internally may be randomly ordered. 

Concerning online learning, the people mostly referred to a data stream, hence a online learning is always incremental learning but incremental learning does not have to be online. 

These are quite fuzzy definitions, and in my opinion there is not clear definition though. I still hope that helps.