When facing difficult real-world applications, it is often unlikely that an individual learning paradigm can actually yield the solution sought (in spite of its theoretical generality) without a strong co-operation with other, profoundly different modules building up the overall system. For instance, artificial neural networks are known to be mathematically "universal" machines, but satisfactory solutions to complex tasks can hardly be achieved with a single feed-forward connectionist architecture. Historically, this led to the development of multiple neural network systems, namely mixtures of experts or neural ensembles, taking benefit from the specialization of individual nets over specific regions of the feature space, according to a divide-and-conquer strategy. As an alternative, multiple classifier systems were proposed, aiming at combining models that have different nature (e.g., generalized linear discriminants, parametric probabilistic models, neural nets) or aim (e.g., estimating a discriminant function, or a class-posterior probability, or a likelihood). In other circumstances, like in the case of hybrid hidden Markov model/connectionist approaches, the combination between the underlying paradigms relies on the idea of exploiting certain general properties of one of them (e.g., the capability of modeling the long-term dependencies in HMMs) with the strength of the other to accomplish local, specific tasks that occur within the former (e.g., the capability of flexible, discriminative modeling of the HMM emission probabilities via neural nets). Along a similar direction, hybrid random fields were introduced recently, They combine the overall, general structure of a Markov random field with the optimal fit of conditional probabilities of individual variables given their Markov blanket as obtained via Bayesian networks. Again, maximum echo-state likelihood networks (MESLiN) were proposed for sequence processing, relying on the combination of the reservoir of an echo-state architecture with a parametric model of the probability density function of the states of the reservoir. Strictly related areas concern the integration between symbolic and sub-symbolic learning machines, and the so-called information-fusion. In all these scenarios, researchers are mostly concerned with the development and investigation of plausible, mathematically sound techniques for combining the different learners in a feasible, robust manner (instead of just piling-up the different modules onto one another heuristically). Such research efforts are leading to training algorithms that split properly the original learning problem over the component machines, training the latter ones according to a joint, global criterion which fits the solution of the original, overall problem.

Ain of this Invited Session is to bring together researchers involved in any area of pattern recognition and machine learning that is related to these issues. Fellow scientists are invited to submit their paper(s) to this Session, according to the guidelines for Authors and the reviewing procedures which hold for the KES Conference hosting this Session. Novel, fresh ideas are particularly welcome (even though in preliminary form), although strong experimental analysis of established approaches to severe real-world tasks is encouraged as well.

Topics of interest include (but they are not limited to):

• multiple classifiers/regression models;
• hybrid hidden Markov models/neural network systems;
• hybrid random fields and other hybrid graphical models;
• combination of kernel machines and other paradigms;
• probabilistic interpretation of neural networks;
• learners based on both symbolic and sub-symbolic paradigms;
• information fusion;
• alternate/mixed induction/deduction learners;
• multitask learning;
• semi-supervised learning;
• hybrid approaches to relational learning and graph/structure processing;
• deep architectures which hybridize supervised and unsupervised learning.

Submission:

Please submit your paper through the KES submission system (PROSE), making sure you pick up the IS30 Invited Session item from the menu (NOTE: this item is listed in the "invited Sessions" table, not in the "General Sessions" list).

Important Dates:

• 10 May, 2011: Submission deadline (EXTENDED!)
• 20 May, 2011: Notification of acceptance
  
• 1 June, 2011: Deadline for camera-ready papers (via PROSE)
  The final publication files (editable word processor source not PDF) must be uploaded onto PROSE by 1 June 2011.
• 1 June, 2011: Early Registration Deadline
  Every paper must have at least one author who has registered for the conference with payment by the Early Registration Deadline for the paper to appear in the proceedings.
• 12, 13, and 14 September 2011: Session

Review process:

Publication:

Joint event:

Session Chairs: