AI 2012 - Special Issue in Machine Learning

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Category AI 2012

Deadline: December 31, 2011 | Date: January 10, 2012

Venue/Country: Online, Online

Updated: 2011-10-23 18:34:46 (GMT+9)

Call For Papers - CFP

Special Issue in Machine Learning

BACKGROUND

Methods for learning and predicting preference models from explicit or

implicit preference information and feedback are among the very recent

research trends in machine learning and knowledge discovery. Approaches

relevant to this area range from learning special types of preference

models such as lexicographic orders over collaborative filtering

techniques for recommender systems and ranking techniques for

information retrieval, to generalizations of classification problems

such as label ranking. Like many complex learning tasks that have

recently entered the stage in the field of machine learning, preference

learning deviates strongly from the standard machine learning problems

of classification and regression. It is particularly challenging as it

involves the prediction of complex structures, such as weak or partial

order relations, rather than single values. Moreover, training input

will not, as it is usually the case, be offered in the form of complete

examples but may comprise more general types of information, such as

relative preferences or different kinds of indirect feedback. Authors

are invited to submit full papers presenting original results on any

aspect of machine learning and games. An ideal contribution to this

special issue would be strongly motivated by applications to commercial

or classical games and focused on research issues relevant to the topics

described below. Papers specific to game theory should not be submitted

to this special issue (there will be forthcoming special issue on this

topic).

SCOPE

Topics of interest to the special issue include, but are not limited to

* quantitative and qualitative approaches to modeling preferences and

different forms of feedback and training data;

* learning utility functions and related regression problems;

* preference mining, preference elicitation, and active learning;

* learning relational preference models;

* generalizations or special forms of classification problems, such as

label ranking, ordinal classification, and hierarchical classification;

* comparison of different preference learning paradigms (e.g.,

learning of single models vs. modular approaches that decompose the

problem into subproblems);

* ranking problems, such as learning to rank objects or to aggregate

rankings;

* methods for special application fields, such as web search,

information retrieval, electronic commerce, games, personalization,

or recommender systems.

SUBMISSIONS

Titles and Short Abstracts: /December 31, 2011/

Submission Deadline: /January 10, 2012/

If you intend to submit a paper to the special issue, please send a

short abstract per E-mail to both editors before December 31, 2011.

Submissions to the special issue must be submitted like regular

submissions to the journal. Instructions can be found at

<http://www.springer.com/computer/ai/journal/10994>.

Each submission will be reviewed according to the standards of the

Machine Learning Journal. All inquiries regarding this special issue

should also be directed to the guest editors.

We aim for a publication of the special issue in late 2012/early 2013.

GUEST EDITORS

Eyke Hüllermeier (Philipps-Universität Marburg)

Johannes Fürnkranz (TU Darmstadt)