IJCNN 2012

Special Session on Active, Incremental and Autonomous Learning: Algorithms and Applications (AIAL)

IJCNN 2012 Special Session on Active, Incremental and Autonomous Learning: Algorithms and Applications (AIAL)




Much of machine learning and data mining has been so far concentrating on analyzing data already collected, rather than collecting data. While experimental design is a well-developed discipline of statistics, data collection practitioners often neglect to apply its principled methods. As a result, data collected and made available to data analysts, in charge of explaining them and building predictive models, are not always of good quality and are plagued by experimental artifacts. Solving the problems involved in data collection and classification will lead to the development of new machine learning algorithms able to address more realistic problems in autonomous and incremental learning.

This special session aims to offer a meeting opportunity for academics and industry-related researchers, belonging to the various communities of *Computational Intelligence*, *Machine Learning*, *Vision systems*, *Experimental Design*, *Data Visualization* and *Data Mining* to discuss new areas of active, incremental and autonomous learning, and to bridge the gap between data acquisition or experimentation and model building. Research papers about algorithms acceleration with hardware are also welcome.

Topics of interest to the workshop include (but are not limited to):
  • Active Learning
  • Unsupervised Learning
  • Self-Taught Learning
  • Semi-Supervised Learning
  • Autonomous Learning
  • Autonomous Intelligent Systems
  • Learning from Unlabeled Data.
  • Agent and Multi-Agent Systems
  • Novelty Detection
  • Agent and Multi-Agent Systems
  • Active, incremental and autonomous learning applied to:
    • computer vision and image understanding
    • robotics
    • privacy, security and biometrics
    • industry
    • human-computer interaction
    • ambient intelligence
    • data visualization: CT and MRI data, seismic survey data, computational fluid dynamic (CFD) data...
  • Hardware acceleration of learning algorithms with multicore and multiprocessor architectures
AIAL . 29 September 2011