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\workshopHeader{Active Learning in Real-world Applications}
{Laurent Candillier, Max Chevalier and Vincent Lemaire} % organisers
{10:30--16:00} % please use this time format everywhere
{Room: Wills, 3.32}
{Friday}

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\workshopIntro{This workshop aims to offer a meeting opportunity for academics and industry-related researchers, belonging to the various communities of Computational Intelligence, Machine Learning, Experimental Design and Data Mining to discuss new areas of active learning, and to bridge the gap between data acquisition or experimentation and model building. How active sampling, incremental learning and data acquisition, can contribute towards the design and modeling of highly intelligent machine learning systems? 

Machine learning indicates methods and algorithms which allow a model to learn a behavior thanks to examples. Active learning gathers methods which select examples used to build a training dataset for the predictive model. All the strategies aim to use a set of examples as small as possible and to select the most informative examples.

When designing active learning algorithms for real-world data, some specific issues are raised. The main ones are scalability and practicability. Methods must be able to handle high volumes of data, in spaces of possibly high-dimension, and the process for labeling new examples by an expert must be optimized.

We encouraged papers that describe applications of active learning in real-world. The industrial context, the main difficulties met and the original solution developed, had to be described. An associated challenge\hfill\break (http://www.nomao.com/labs/challenge) has been conjointly organized on a practical application of active learning. The challenge and the results obtained will be presented.}

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\timeTitlePeople{10:30--11:30}{Invited Talk: Active Learning for Discovery in the Laboratory: Characterising Biomolecular Systems}{Chris Lovell}

\timeTitlePeople{11:30--12:15}{Challenge Presentation: Design and Analysis of the Nomao challenge: Active Learning in the Real-World}{Laurent Candillier and Vincent Lemaire}

\timeTitlePeople{12:15--12:45}{Challenge winner: Batch-Mode Active Learning by Using Misclassified Data}{Tengyu Sun and Jie Zhou}

\timeTitlePeople{12:45--13:15}{Position paper: Programmer's Active Learning: A Broader Perspective of Choices for Real-World Classification Tasks that Matter}{George Forman}

\timeTitle{13:15--14:45}{Lunch (not provided)}

\timeTitlePeople{14:45--15:15}{Incorporating Density in Active Learning with Application to Ranking}{Wenbin Cai and Ya Zhang}

\timeTitlePeople{15:15--15:45}{Active Learning in the Spatial-domain for Landslide Mapping in Remote Sensing Images}{Andre Stumpf, Nicolas Lachiche, Jean-Philippe Malet, Norman Kerle and Anne Puissant}

\timeTitle{15:45--16:00}{Participants Open Discussion}

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