In this paper, we present the systems developed by GTMUVigo team for the Multimedia Person Discovery in Broadcast TV task at MediaEval 2015. The systems propose two different strategies for person discovery in audio through speaker diarization (one based on an online clustering strategy with error correction using OCR information and the other based on agglomerative hierarchical clustering) as ...

This paper presents an evaluation of a derivative-free adaptive controller with optimal control modification on the NASA Generic Transport Model. The proposed controller has a derivative-free form which is expected to provide faster adaptation and smoother error transients particularly for situations where GTM dynamics undergo a sudden change. This paper shows that robustness is improved while ...

This paper describes the machine translation (MT) system developed by the Transducens Research Group, from Universitat d’Alacant, Spain, for the WMT 2011 shared translation task. We submitted a hybrid system for the Spanish–English language pair consisting of a phrase-based statistical MT system whose phrase table was enriched with bilingual phrase pairs matching transfer rules and dictionary e...

Most machine-learning algorithms are designed for datasets with features of a single type whereas very little attention has been given to datasets with mixed-type features. We recently proposed a model to handle mixed types with a probabilistic latent variable formalism. This proposed model describes the data by type-specific distributions that are conditionally independent given the latent spa...

A kernel version of Generative Topographic Mapping, a model of the manifold learning family, is defined in this paper. Its ability to adequately model non-i.i.d. data is illustrated in a problem concerning the identification of protein subfamilies from protein sequences.

This thesis is concerned with the construction of topographic maps of structured data. A probabilistic generative model-based approach is taken, inspired by the GTM algorithm. Depending on the data at hand, the form of a probabilistic generative model is specified that is appropriate for modelling the probability density of the data. A mixture of such models is formulated which is topographical...

A new tool for discovering and visualizing interactions between scientific publication domains and industrial patent domains is introduced. The tool is applied to a database of the United States patent data (USPTO) from 1980 till 1995 and the Science Citation Index (SCI) bibliographic databases. The core algorithm behind our tool is the kernel-based Maximum Entropy Rule (kMER), a learning schem...

Recently there has been an outburst of interest in extending topographic maps of vectorial data to more general data structures, such as sequences or trees. However, there is no general consensus as to how best to process sequences using topographic maps, and this topic remains an active focus of neurocomputational research. The representational capabilities and internal representations of the ...

During an 11 days mission in February 2000 the Shuttle Radar Topography Mission (SRTM) collected data over 80% of the Earth's land surface, for all areas between 60 degrees N and 56 degrees S latitude. Since SRTM data became available, many studies utilized them for application in topography and morphometric landscape analysis. Exploiting SRTM data for recognition and extraction of topographic ...

This paper compares the performance of topographic analysis and binary analysis for recognition of digits in hydrographic maps. Each of the methods processed the input image by extracting binary print components, recognizing long lines, splitting touching digits, and, finally, recognizing individual symbol candidates. The topographic analysis extracted the information by computing topographic l...