AIMS AND SCOPE
Available training datasets are increasingly being gathered by evolution of web and sensor networks, and so on.
Traditional many machine learning algorithms are focused on batch learning from a static dataset or from a well-known distribution.
However, these bach algorithms take a lot of time to learn a large amount of training data and many batch learning algorithms are not adapt to deal with nonstationary distributions (i.e. data streams/flows).
On-line Incremental algorithms process few examples at a time and allows to extract the knowledge structure from continuous data in real-time.
This problem became more difficult when we deal with high dimensional data, unbalanced data or outliers.
This Special Session aims to act as a forum for new ideas and paradigms concernig the Incremental Learning (non-stationary learning).
This session would solicit theoretical and applicative research papers including but not limited to the following topics :
Theory:
- Incremental Supervised Learning
- Incremental Unsupervised Learning
- Online Learning
- Online Feature Selection
- Clustering data stream
- Distributed Clustering
- Consensus Clustering
- Incremental Probabilistical Models
- Active Learning
- Application:
- Incremental learning for data mining
- Incremental learning for computer vision and speech processing
- Incremental learning for web
- Incremental learning for robotics