
Chapter 1
Knowledge Discovery from Data
Streams
1.1 Introduction
In the last three decades, machine learning research and practice have
focused on batch learning usually using small datasets. In batch learning, the
whole training data is available to the algorithm, which outputs a decision
model after processing the data eventually (or most of the times) multiple
times. The rationale behind this practice is that examples are generated at
random according to some stationary probability distribution. Most learners
use a greedy, hill-climbing search in the space of models. They are prone
to high-variance and overfitting problems. Brain and Webb (2002) poin ...