Random forests (RF) is a supervised machine learning algorithm, which has recently started
to gain prominence in water resources applications. However, existing applications are …

In many applications of information systems learning algorithms have to act in dynamic
environments where data are collected in the form of transient data streams. Compared to …

The random forest algorithm, proposed by L. Breiman in 2001, has been extremely
successful as a general-purpose classification and regression method. The approach, which …

Data preprocessing and reduction have become essential techniques in current knowledge
discovery scenarios, dominated by increasingly large datasets. These methods aim at …

Recently, incremental and on-line learning gained more attention especially in the context of
big data and learning from data streams, conflicting with the traditional assumption of …

Incremental learning refers to learning from streaming data, which arrive over time, with
limited memory resources and, ideally, without sacrificing model accuracy. This setting fits …

Decision tree ensembles are an extremely popular tool for obtaining high-quality predictions
in nonparametric regression problems. Unmodified, however, many commonly used …

With the increasing demand for real-time analytics and decision making, anomaly detection
methods need to operate over streams of values and handle drifts in data distribution …

Neural networks augmented with external memory have the ability to learn algorithmic
solutions to complex tasks. These models appear promising for applications such as …

This paper develops a new neural network architecture for modeling spatial distributions (ie
distributions on R d) which is more computationally efficient than a traditional fully …