S2 (2010-2011)

We possess a wealth of prior knowledge about most prediction problems, and particularly so for many of the fundamental tasks in natural language processing. Unfortunately, it is often difficult to make use of this type of information during learning, as … Read More

Inductive Logic Programming (ILP) is a Machine Learning approach with foundations in Logic Programming. The problem specification and the models discovered by ILP systems are both represented as Prolog programs allowing for great expressiveness and flexibility. However, this flexibility comes … Read More

In the first half of the talk, I will give an overview on structured prediction, a general framework which encompasses many learning formalisms, such as those underlying hidden Markov models, conditional random fields, and structured support vector machines. Applications abound … Read More

Designing a component, process or a control system to achieve minimum or maximum of a single objective (or goal) often results in a single optimum solution describing the shape, dimensions, process or strategy of solving the task. Although such an … Read More

The electrocardiographic (ECG) signal has been shown to contain relevant information for human identification. Even though results validate the potential of these signals, data acquisition methods and apparatus explored so far compromise user acceptability, requiring the acquisition of ECG at … Read More

Surveillance systems aim to characterize human activities and to detect abnormal behaviors. This task is specially challenging if the camera field of view is wide and the objects are far from the camera. In such operating conditions, it is not … Read More

In many applications, we deal with high dimensional datasets with sparse data (many features have zero value with high probability). For instance, in text classification and information retrieval problems, we have large collections of documents. Each text is usually represented … Read More

Genetic Programming (GP) is the youngest paradigm inside the Artificial Intelligence field called Evolutionary Computation. Created by John Koza in 1992, it can be regarded as a powerful generalization of Genetic Algorithms, but unfortunately it is still poorly understood outside … Read More

The thesis addresses two important nonlinear inverse problems in image processing: the separation of show-through and the bleed-trough mixtures and the blind deblurring of images. New solutions to cope with their high levels of indetermination are proposed. Two separation methods … Read More

Numerous signals arising from physiological and physical processes are not only non-stationary but also posses a mixture of sustained oscillations and non-oscillatory transients that are difficult to disentangle by linear methods. Examples of such signals include speech, biomedical and geophysical … Read More