Objective
Learning classifiers automatically from examples is subject to the
multidisciplinary field of machine learning.
The structured output learning (SOL) is concerned with the
learning of classifiers for prediction of multiple
interdependent variables exhibiting some structure dependence.
Recent progress in SOL focuses mainly on supervised methods
that require labeled examples. A high cost of labeled examples
significantly limits application of SOL to many domains.
Our goal is threefold. First, to developed framework for semi-supervised SOL from cheap partially labeled examples. Second, to apply this new framework to two important SOL tasks: (i) Markov Networks learning and (ii) learning of 2-dimensional image grammars. Third, to use the new algorithms for solving computer vision problems including the image segmentation and the car license plate recognition.
To achieve the first goal, we will examine two strategies. First, we will
combine powerful discriminative methods for SOL with generative models offering a principled way to deal with missing labels. Second, we will extend the existing semi-supervised methods in order to handle the partially labeled examples.
To achieve the second goal, we will incorporate the existing methods for
supervised SOL of Markov Networks and 2D grammars to the framework
developed as the first goal.
To achieve the third goal, we will build on the technology for
image segmentation and license plate recognition developed by
the host. The currently used classification methods will be
replaced by the developed semi-supervised SOL algorithms to
demonstrate their effectiveness on real-life problems.
Achieving the goals will be possible by joining the expertise
of the applicant and the host. This applies both to theoretical
and application oriented goals. The applicant is experienced in
SOL and Markov Networks while the host will complement this
with a large expertise in 2D grammars and computer
vision.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences computer and information sciences artificial intelligence computer vision
- natural sciences computer and information sciences artificial intelligence generative artificial intelligence
- natural sciences computer and information sciences artificial intelligence machine learning
You need to log in or register to use this function
We are sorry... an unexpected error occurred during execution.
You need to be authenticated. Your session might have expired.
Thank you for your feedback. You will soon receive an email to confirm the submission. If you have selected to be notified about the reporting status, you will also be contacted when the reporting status will change.
Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
FP7-PEOPLE-ERG-2008
See other projects for this call
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Coordinator
160 00 PRAHA
Czechia
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.