**Paolo Frasconi & Alessandro Sperduti**

The purpose of the tutorial is to examine the state of the art in the use of connectionist networks for processing data structures and to present a unified view of formalisms and tools for dealing with rich data representations, covering connectionist architectures for data structures, learning algorithms, and applications. In particular, we will show that it is possible to represent and classify structured information very naturally. Moreover, it is possible to formalize several supervised models for classification of structures which stem very naturally from well known models, such as back propagation through time networks, real-time recurrent networks, simple recurrent networks, recurrent cascade correlation networks, and neural trees.

Because many concepts and formal tools are inherited from the theoretical framework of recurrent networks for sequence processing, the tutorial will begin with a review of basic concepts underpinning recurrent neural networks, for those attendees which are not familiar with such a class of models. Algorithms for training recursive neural networks are presented as generalizations of gradient computation algorithms for recurrent nets, and complexity as well computational issues are discussed. Finally, examples of applications in chemistry, structural pattern recognition, and theorem proving are presented.

**Prerequisite knowledge: **

Although we will briefly review the essential concepts for data
structure and neural networks, we will assume that the attendants will
be familiar with data structures; we also assume basic knowledge of
linear algebra and calculus for the treatment of some neural network
paradigms.

**Paolo Frasconi**
received the M.Sc. degree in Electronic Engineering in
1990 and the Ph.D. degree in Computer Science in 1994, both from the
University of Florence, Italy. He is currently Associate Professor
with Dipartimento di Ingegneria Elettrica ed Elettronica at the
University of Cagliari, Italy. He was Assistant Professor with the
Dipartimento di Sistemi e Informatica at the University of Florence,
Italy. In 1992 he was a Visiting Scholar in the Department of Brain
and Cognitive Science at the Massachusetts Institute of Technology,
Cambridge. In 1994 he was a Visiting Scientist at Centro Studi e
Laboratori Telecomunicazioni (CSELT), Turin. His current research
interests include neural networks, Markovian models, and graphical
models, with particular emphasis on problems involving learning about
sequential and structured information. Paolo Frasconi is the author of
around 50 refereed papers mainly in the areas of graphical models for
learning, neural networks, pattern recognition, artificial
intelligence.

**Alessandro Sperduti**
received his university education from the
University of Pisa, Italy ("Laurea" and Doctoral degrees in 1988 and
1993, respectively, all in Computer Science). In 1993 he spent a
period at the International Computer Science Institute, Berkeley,
supported by a postdoctoral fellowship. In 1994 he moved back to the
Computer Science Department, University of Pisa, where he was
Assistant Professor, and where he presently is Associate
Professor. His research interests include pattern recognition, image
processing, neural networks, hybrid systems. In the field of hybrid
systems his work has focused on the integration of symbolic and
connectionist systems. He contributed to the organization of several
workshops on this subject and he served also in the program committee
of conferences on neural networks. Alessandro Sperduti is the author
of around 50 refereed papers mainly in the areas of neural networks,
fuzzy systems, pattern recognition, and image processing.

Webmaster: Sven Olofsson, sveno@dsv.su.se Last modified: Mar 16, 1999