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PinView

Personal Information Navigator Adapting Through Viewing (PinView) of the Statistical Machine Learning and Bioinformatics group is a research project funded by EU FP7, in collaboration with University of Southampton, University College London, University of Leoben, Xerox Research Centre Europe, and celum gmbh.

The PinView consortium combines pioneering application expertise with a solid machine learning background in content-based information retrieval.

MULTIBIO

Computational data fusion of multiple biological information sources and background data (MULTIBIO) of the Statistical Machine Learning and Bioinformatics group is a project funded by TEKES, under the Algorithmic Data Analysis research programme.

TRANSCENDO

The Computational Translation from Model Organisms to Humans (TRANSCENDO) of the Statistical Machine Learning and Bioinformatics group is a project funded by TEKES MASI program, under the Algorithmic Data Analysis research programme, in collaboration with VTT Biotechnology, VTT Information Technology and University of Turku.

UI-ART

Urban contextual information interfaces with multimodal augmented reality (UI-ART) of the Statistical Machine Learning and Bioinformatics group is a research project funded by TKK under the Multidisciplinary Institute for Digitalisation and Energy (MIDE) research programme.

We will develop methods and a pilot system for context-dependent search and presentation of information with the means of augmented reality.

SULRSL

Supervised Unsupervised Learning and Relevant Subtask Learning (SULRSL) of the Statistical Machine Learning and Bioinformatics group is a project funded by the Academy of Finland.

The project develops statistical machine learning methods to extract from high-dimensional data sets regularities that are relevant to the analyst. We infer relevance from auxiliary information that comes with the data, such as class labels coupled with input samples.

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