IJCNN 2015

2nd International Workshop on Advances in Learning from/with Multiple Learners (ALML 2015)

July 17 - Killarney, Ireland

Deadline : January 15, 2015
Thursday, July 16th, 2015 from 1:30 PM in Ballroom  




This workshop will cover original and pioneering contributions, theory as well as applications on creating and combining learning models, and aim at an inspiring discussion on the recent progress and the future developments. Learners based on different paradigms can be combined for improved accuracy. Each learning method presupposes some model of the world that comes with a set of assumptions which may lead to error if they do not hold. Learning is an ill-posed problem and with finite data each algorithm converges to a different solution and fails under various circumstances. In learning models combinations, it is possible to make a distinction between two main modes: ensemble and modular. For an ensemble approach, several solutions to the same task, or task component, are combined to yield a more reliable estimate. In the modular approach, particular aspects of a task are dealt with by specialist components before being recombined to form a global solution. In this workshop, the reasons for combining learning models and the main methods for creating and combining them will be presented. Also, the effectiveness of these methods will be discussed considering the concepts of diversity and selection of these approaches. The workshop will strive to bring together the practitioners of these approaches in an attempt to study a unified framework under which these interactions can be studied, understood, and formalized.
The following is a partial list of relevant topics (not limited to) for the workshop:
       - Bagging approaches
       - Boosting techniques
       - Collaborative clustering
       - Collaborative learning
       - Cooperative learning
       - Ensemble methods
       - Hybrid systems
       - Mixtures of distributions
       - Mixtures of experts
       - Modular approaches
       - Multi-task learning
       - Multi-view learning
       - Task decomposition
       - Transfer learning with multiple sources
       - Learning from data streams
       - Data aggregation