Abstract
(Englisch)
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The objective is to build a Europe-wide Distributed Institute which will pioneer principled methods of pattern analysis, statistical modelling, and computational learning as core enabling technologies for multimodal interfaces that are capable of natural and seamless interaction with and among individual human users.
At each stage in the process, machine learning has a crucial role to play. It is proving an increasingly important tool in Machine Vision, Speech, Haptics, Brain Computer Interfaces, Information Extraction and Natural Language Processing; it provides a uniform methodology for multimodal integration; it is an invaluable tool in information extraction; while on-line learning provides the techniques needed for adaptively modelling the requirements of individual users. Though machine learning has such potential to improve the quality of multimodal interfaces, significant advances are needed, in both the fundamental techniques and their tailoring to the various aspects of the applications, before this vision can become a reality.
We therefore propose to establish an inter-disciplinary Europe-wide Distributed Institute of Pattern Analysis, Statistical Modelling, and Computational Learning. The Institute will foster interaction between groups working on fundamental analysis including statisticians and learning theorists; algorithms groups including members of the non-linear programming community; and groups that act as bridges to the application domains and end-users. It will cover the full range of modern well-founded methods of machine learning that allow rigorous statistical and algorithmic analysis, while focussing on those aspects relevant to the applications.
Application groups will create bridges between the machine learning research of PASCAL and the domains of machine vision, speech, haptics, brain-computer interfaces, natural language processing, information-retrieval, textual information access and user modelling for computer human interaction.
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