NIPS 2002 Workshop
Unreal Data: Principles of Modeling Nonvectorial Data
A large amount of research in machine learning is concerned with classification and regression for real-valued data which can easily be embedded into a Euclidean vector space. This is in stark contrast with many real world problems, where the data is often a highly structured combination of features (e.g., natural language and speech processing), a sequence of symbols (e.g., bioinformatics), a mixture of different modalities, may have missing variables, etc. The items in non-vectorial data sets can be one dimensional structures (e.g. sequences), two dimensional (e.g. images), three dimensional (e.g. molecular descriptions), trees (e.g. xml documents), or other hybrid and not-so-easily classified data structures.
To address the problem of learning from non-vectorial data, various methods have been proposed, such as embedding the structures in Hilbert spaces (e.g., via Kernels), the extraction and selection of features, proximity based approaches, parameter constraints in Graphical Models, Inductive Logic Programming, Decision Trees, or clever hand-crafted models.
Aims of this workshop: The goal of this workshop is twofold. Firstly, we hope to make the machine learning community aware of the problems arising from domains where non-vectorspace data abounds and to uncover the pitfalls of mapping such data into vector spaces. Secondly, we will try to find a more uniform structure governing methods for dealing with non-vectorial data and to understand what, if any, are the principles underlying the modeling of non-vectorial data.
Date and Location
The workshop will be held in Whistler, British Columbia (Canada) on Friday, December 13th 2002.
Schedule and List of Speakers
Getting in Touch
If you are interested in contributing or have comments please send an e-mail to any one of the organizers or fax to +61-2-612-58650 (Alex Smola or Gunnar Rätsch)