We would like to announce the new workshop organized on this "topic" :
Interactive Adaptive Learning (IAL2017)
Co-Located With The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML PKDD 2017)
18 September 2017 - Skopje (Macedonia)
All details :

NEWS ! You may find the agenda (program) of the workshop [here ...]

The workshop will be in room number five ! . Proceedings are online : http://ceur-ws.org/Vol-1707/

[download the CFP...]

We invite submissions for the Workshop Active Learning: Applications, Foundations and Emerging Trends", which is part of the International Conference on Knowledge Technologies and Data-driven Business (i-KNOW). The i-KNOW has a 15-year history of bringing together the best minds from science and industry. Since 2001, this international conference is annually held in Graz, Austria. It has successfully brought together leading researchers and developers from these fields and attracted over 500 international attendees every year.

Aims: : Active Learning addresses the intersection between Data Mining/Machine Learning and interaction with humans. Aiming at optimizing this interaction, it bridges the gap between data-centric and user-centric approaches. For example, by requesting the most relevant information or performing the most informative experiment. Facing big volumes of data but limited human annotation and supervision capacities, active approaches become increasingly important for improving the efficiency in interactions.

Active learning is a very useful methodology in on-line industrial applications for reducing efforts for sample annotation and measurements of ``target'' values (e.g., quality criteria). It further reduces the computation speed of machine learning and data mining tools, as embedded models are only updated based on a subset of samples selected by the implemented active learning technique. This is especially important when performing modeling and mining cycles from on-line data streams, where real-time demands to model updates and inference processing are quite usual.

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 labeling 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.

Therefore, contributions on active learning are welcome that address aspects including, but not limited to:
Important dates: Important - SUBMISSION INSTRUCTIONS:

PROGRAM COMMITTEE (to be extended...) Venue

The workshop is co-located with i-KNOW 2016 (http://i-know.tugraz.at/)

Messe Congress Graz
Messeplatz 1
8010 Graz