-------- Weitergeleitete Nachricht -------- Betreff: [WI] CfP: Workshop Active Learning: Applications, Foundations and Emerging Trends Datum: Thu, 30 Jun 2016 20:32:02 +0200 Von: Georg Krempl georg.krempl@iti.cs.uni-magdeburg.de Antwort an: Georg Krempl georg.krempl@iti.cs.uni-magdeburg.de Organisation: Otto von Guericke University Magdeburg An: kdml@cs.uni-kassel.de, ak-kd-list@lists.uni-karlsruhe.de, wi@aifb.uni-karlsruhe.de
****************************************************************************** * Submission Deadline: 25 July 2016 * ******************************************************************************
We are pleased to invite submissions for the Workshop ** Active Learning: Applications, Foundations and Emerging Trends **
taking place as part of the International Conference on Knowledge Technologies and Data-driven Business (i-KNOW) in October, 17-19th 2016 in Graz, Austria.
Important dates: - Deadline for submissions: July 25 23:59 CEST, 2016 - Notification of acceptance: August 22, 2016 - Camera ready submission: September 12, 2016 - Workshop in Graz: October 17-19, 2016
Submission instructions: - Page limit: 2-4 pages (excluding references) - Submission via EasyChair: https://easychair.org/conferences/?conf=alatiknow2016 - Single-blinded review process, papers need not to be anonymized - At least one author is required to register for i-KNOW. - Contributions are published in open access workshop proceedings, and presented in a spotlight talk/discussion and a poster session.
Organizers: - Georg Krempl, University Magdeburg, georg.krempl at ovgu.de - Vincent Lemaire, Orange Labs France, vincent.lemaire at orange.com - Edwin Lughofer, University Linz, edwin.lughofer at jku.at - Daniel Kottke, University Magdeburg, daniel.kottke at ovgu.de
The i-KNOW has a 15-year history of bringing together the best minds from science and industry, attracting over 500 leading researchers and developers each year.
This workshop addresses the intersection between Data Mining/Machine Learning and interaction with humans or expensive oracles. Active learning has shown to be a very useful methodology in on-line industrial applications for reducing efforts for sample annotation and measurements of ``target'' values (e.g., quality criteria), and for reducing the computation speed of machine learning and data mining tools, for example in data streams.
Various approaches, application scenarios and deployment protocols have been proposed for active learning. However, despite the efforts made from academia and industry researchers alike, there are still gaps between research on theoretical and practical aspects. 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 labelling new examples by an expert must be optimized.
The aim of this workshop is to provide a forum for researchers and practitioners to discuss approaches, identify challenges and gaps between active learning research and meaningful applications, as well as define new application-relevant research directions. We encouraged also 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. Industrials with open research questions on active learning may also write a paper to raise the questions to the scientific community.
Thus, contributions on active learning are welcome that address aspects including, but not limited to: - New Active Learning methods big and streaming data - On-line, incremental, single-pass selection techniques - Active Learning in combination with complex model structures or ensemble selection strategies, e.g. deep learning neural networks, extreme learning machines or recurrent neural networks - Active Learning for cost-sensitive applications or imbalanced data - Active Learning with adaptive budget management/stopping criteria. - Combinations with other techniques, e.g. transfer learning or drift detection - Decremental Active Learning with the usage of unlearning techniques. - Active on-line design of experiments, active class or feature selection - Active, user-centric approaches for selection of information, as for example in BCI or crowdsourcing - Innovative use of Active Learning techniques, e.g. for fraud or outlier detection - New interactive learning protocols and application scenarios, - Applications and Real-world deployment of Active Learning techniques - Evaluation of Active Learning and comparative studies
-- Knowledge Management & Discovery Business Information Systems Group Otto-von-Guericke University Magdeburg Building 29, Office 124, Postbox 4120 39016 Magdeburg, Germany georg.krempl at iti.cs.uni-magdeburg.de
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