This International Workshop will cover original and pioneering contributions, theory as well as applications on Machine Leanring, and aim at an inspiring discussion on the recent progress and the future development.

Machine learning, which is one of the fields of study of Artificial Intelligence, is a scientific discipline that uses statistical approaches to give computers the ability to “learn” from data, i. e. to improve their performance in solving tasks without being explicitly programmed for each. Machine Learning is concerned with the development, analysis and implementation of automated methods that allow a machine or application to evolve through a learning process, and thus to perform tasks that are difficult or impossible to perform by more conventional algorithms.
Learning is an ill-posed problem and with finite data each algorithm converges to a different solution and fails under variant circumstances. Learners based on different paradigms can be combined for improved accuracy. Each learning method assumes a certain model that comes with a set of assumptions which may lead to error if the assumptions do not hold.
In this workshop, the reasons for combining learning models and the main methods for creating and combining will be presented. Also, the effectiveness of these methods will be discussed considering the concepts of diversity and selection of these approaches.