A broad range of topics   Guidelines for submission, see below

We invite the submission of abstracts and full papers on all aspects of machine learning and related disciplines, including, but not limited to

  • Kernel Methods
  • Bayesian Learning
  • Case-based Learning
  • Ensemble Methods
  • Computational Learning Theory
  • Data Mining
  • Web/Link Mining
  • Evolutionary Computation
  • Hybrid Learning Systems
  • Graphical Models
  • Inductive Logic Programming
  • Knowledge Discovery in Databases
  • Online Learning
  • Learning in Multi-Agent Systems
  • Learning in Dynamic Domains / Data Streams
  • Multi-Strategy Learning
  • Neural Networks
  • Reinforcement Learning
  • Robot Learning
  • Feature Selection and Dimensionality Reduction
  • Scientific Discovery
  • Multi-Task and Transfer Learning
  • Statistical Learning
  • Ranking / Preference Learning / Information Retrieval
  • Computational models of Human Learning
  • Structured Output Prediction / Learning from Multi-Label Data
  • Learning for Language and Speech
  • Image, Video, Audio and Text Analysis
  • Applications of Machine Learning
  • Learning and Ubiquitous Computing

This year we have chosen for a two-day program, including a workshop day to showcase the use of predictive modeling for the life sciences. This is in part motivated by the increasing focus on applicability of scientific research. In addition to theoretical and applied work in machine learning, we warmly welcome applications in areas such as:

  • Bioinformatics
  • Medicine / Biomedical Engineering
  • Systems Biology / Microbiology
  • Chemometrics / Computational Drug Discovery
  • Ecology / Environmental Sciences
  • Agriculture and Soil Management
  • Food Sciences
  • Geographical Information Systems
  • Weather and Climate Forecasting

Guidelines for submission

One can submit full papers up to 6 pages or one-page abstracts. Please download our templates and stylefiles for preparing your paper or abstract according to the official style. Other formats are not allowed. Full papers need to report original, unpublished work and they are meant for researchers that prefer to obtain an official publication via BeneLearn, including feedback from the program committee. Abstracts can also concern work in progress and past published work, if the precise references to the original publication are mentioned. This way we aim to provide the same broad overview as in previous years, and make it attractive and easy to attend for both senior as well as junior researchers, from both academia and industry. To attract as many people as possible, registration fees will be low and three international speakers are invited for a keynote talk.

We consider a single submission procedure for the main conference and its satellite workshop. The program committee will make a selection for oral presentations among abstracts and full papers to ensure a program that has academic quality but is also interesting and inspiring for the attendees. The remainder of the submissions will be presented during a poster session and poster spotlight presentations. Abstracts and papers should be submitted electronically, no later than Friday March 2 Monday March 12th, 2012. Notification of acceptance is on Friday April 6. Submissions have to be made by means of the EasyChair system. During submission authors will be able to indicate their preference for an oral or a poster presentation. Submissions that concern an application in the life sciences will be considered for PMLS, all other submissions will be considered for the main conference day.

About KERMIT

KERMIT (acronym for Knowledge Extraction, Representation and Management by means of Intelligent Techniques) is an interdisciplinary team of (bio-)engineers, computer scientists and mathematicians, initiated by Bernard De Baets in 2000. It draws upon intelligent techniques resulting from the cross-fertilization between the fields of computational intelligence and operations research. The activities are organized in three highly interwoven research threads: knowledge-based modeling, predictive modeling and spatio-temporal modeling.

Academic Partners

BeneLearn 2012 | 24-25 May 2012 | Gent