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AIAL 2012 - IJCNN 2012 Special Session on Active, Incremental and Autonomous Learning: Algorithms and Applications (AIAL)
Website www.dtic.ua.es/~jgarcia/IJCNN2012/ |
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Category AIAL 2012
Deadline: December 19, 2011 | Date: June 10, 2012-June 15, 2012
Venue/Country: Brisbane, Australia
Updated: 2011-10-25 07:30:07 (GMT+9)
Call For Papers - CFP
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 LearningUnsupervised LearningSelf-Taught LearningSemi-Supervised LearningAutonomous LearningAutonomous Intelligent SystemsLearning from Unlabeled Data.Agent and Multi-Agent SystemsNovelty DetectionAgent and Multi-Agent SystemsActive, incremental and autonomous learning applied to:computer vision and image understandingroboticsprivacy, security and biometricsindustryhuman-computer interactionambient intelligencedata visualization: CT and MRI data, seismic survey data, computational fluid dynamic (CFD) data...Hardware acceleration of learning algorithms with multicore and multiprocessor architecturesKeywords: Accepted papers list. Acceptance Rate. EI Compendex. Engineering Index. ISTP index. ISI index. Impact Factor.
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