In this paper we present a general overview of several supervised machine learning (ML)
algorithms and illustrate their use for the prediction of mass mortality events in the coastal …

Boosting is a class of machine learning methods based on the idea that a combination of
simple classifiers (obtained by a weak learner) can perform better than any of the simple …

The term “boosting” denotes a powerful means of facilitating machine learning that was
invented by the book's authors 20 years ago and intensively developed since. Despite this …

We present a unifying framework for studying the solution of multiclass categorization
problems by reducing them to multiple binary problems that are then solved using a margin …

The optimal treatment of patients with cancer depends on establishing accurate diagnoses
by using a complex combination of clinical and histopathological data. In some instances …

AdaBoost is one of the most excellent Boosting algorithms. It has a solid theoretical basis
and has made great success in practical applications. AdaBoost can boost a weak learning …

1. Introduction to pattern classification. 1.1. Pattern classification. 1.2. Induction algorithms.
1.3. Rule induction. 1.4. Decision trees. 1.5. Bayesian methods. 1.6. Other induction …

We provide an introduction to theoretical and practical aspects of Boosting and Ensemble
learning, providing a useful reference for researchers in the field of Boosting as well as for …

Artificial intelligence (AI) is a scientific discipline that aims to create intelligent machines.
Machine learning is a popular and practical AI subfield that aims to automatically improve …

Many supervised machine learning tasks can be cast as multi-class classification problems.
Support vector machines (SVMs) excel at binary classification problems, but the elegant …