Pattern analysis, statistical modelling and computational Learning

Objective

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 multi-modal 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 multi-modal 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 multi-modal 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 in machine vision, speech, haptics, brain-computer interfaces, natural language processing, information-retrieval, textual information processing and user modelling for computer-human interaction, groups that will act as bridges to the application domains and end users.

Programme(s)

• FP6-IST - Information Society Technologies: thematic priority under the specific programme "Integrating and strengthening the European research area" (2002-2006).

Topic(s)

• IST-2002-2.3.1.6 - Multimodal interfaces

Funding Scheme

Coordinator

Participants (56)