The classification problem is closely related to the clustering problem discussed in Chaps. 6
and 7. While the clustering problem is that of determining similar groups of data points, the …

In supervised machine learning, an agent is typically trained once and then deployed. While
this works well for static settings, robots often operate in changing environments and must …

With the advance in both hardware and software technologies, automated data generation
and storage has become faster than ever. Such data is referred to as data streams …

In the last two decades, machine learning research and practice has focused on batch
learning, usually with small datasets. Nowadays there are applications in which the data are …

One of the most popular tools for mining data streams are decision trees. In this paper we
propose a new algorithm, which is based on the commonly known CART algorithm. The …

Online prediction has become one of the most essential tasks in many real-world
applications. Two main characteristics of typical online prediction tasks include tabular input …

We introduce a novel incremental decision tree learning algorithm, Hoeffding Anytime Tree,
that is statistically more efficient than the current state-of-the-art, Hoeffding Tree. We …

The problem of real-time extraction of meaningful patterns from time-changing data streams
is of increasing importance for the machine learning and data mining communities …

A new approach to the online classification of streaming data is introduced in this paper. It is
based on a self-develo** (e volving) fuzzy-rule-based (FRB) classifier system of T akagi-S …

Classification and regression tree learning on massive datasets is a common data mining
task at Google, yet many state of the art tree learning algorithms require training data to …