S3 (2011-2012)

Towards automatic identification of independent components representing EEG artifacts

Independent component analysis (ICA) is a blind source separation technique that allows the separation of linear mixtures of signals into maximal statistically independent sources, normally called independent components (ICs). This technique relies on several mathematical assumptions which need to be … Read More

Cross-Language Alignments: Challenges, Guidelines and Gold Sets

In this presentation I will describe the key cross-language annotation guidelines to provide support for state-of-the art machine translation systems. The guidelines aim at improving the quality of the statistical machine translation output by using linguistically-informed and motivated annotation of … Read More

Modeling Subcellular Location from Images and Other Sources of Information

Subcellular location is an important property of proteins, carefully regulated by the cell machinery. To determine subcellular location on a proteome-wide scale, fluorescent image data is most commonly used and a classification system is employed for analysis. These systems assign … Read More

A New Approach to Cross-Modal Multimedia Retrieval

The problem of cross-modal retrieval from multimedia repositories is considered. This problem addresses the design of retrieval systems that support queries across content modalities, e.g. using text to search for images. A mathematical formulation is proposed, equating the design of … Read More

Distributed Algorithms for Separable Optimization

In this talk we are interested in distributed algorithms for solving separable optimization problems. Many problems in engineering can be formulated as separable optimization problems, i.e., minimizing the sum of P functions subject to the intersection of P sets. Our … Read More

Feature discretization and selection techniques for high-dimensional datad

High-dimensional datasets are increasingly common in learning problems, in many different domains, such as text categorization, genomics, econometrics, and computer vision. The excessive number of features carries the problem of memory usage in order to represent and deal with these … Read More

The dissimilarity representation for structural pattern recognition

The patterns in collections of real world objects are often not based on a limited set of isolated properties such as features. Instead, the totality of their appearance constitutes the basis of the human recognition of patterns. Structural pattern recognition … Read More

Unsupervised Learning of Finite Mixture Models using Mean Field Games

In this presentation we develop a dynamic continuous solution to the clustering problem of data characterized by a mixture of K distributions, where K is given a priori. The proposed solution resorts to game theory tools, in particular mean field … Read More

Integration of Fourier Domain Speech Enhancement and Automatic Speech Recognition through Uncertainty Propagation

Speech enhancement techniques aim to recover the original clean signal underlying corrupted speech. Such techniques typically operate in the short-time Fourier transform (STFT) domain where phenomena like additivity of background noises, interfering speakers and echoes are easier to model. By … Read More

Integrating machine learning functionality into a real-time data processing engine

How to detect and classify credit card fraud? What is fraud for a person, say, buying expensive jewelry, spending on online casinos or simply spending a lot of money, is just the regular day-to-day for another. To make matters worse, … Read More