# Mandzuik BISC Seminar rescheduled: 23 June 1997

Michael Lee (leem@cs.berkeley.edu)
Fri, 20 Jun 1997 17:35:22 +0200

Dear Bisc-Group,

The BISC Seminar given by Dr. Mandziuk has been rescheduled for Monday,
the 23rd of June in room 320 Soda Hall. Attached is the copy of his
abstract and title.

Michael Lee

--
_______________________________________________________________________
Michael A. Lee
Berkeley Initiative in Soft Computing
387 Soda Hall                                      Tel: +1-510-642-9827
Computer Science Division                          Fax: +1-510-642-5775
University of California                    Email: leem@cs.berkeley.edu
Berkeley, CA 94720-1776 USA       WWW: http://www.cs.berkeley.edu/~leem
_______________________________________________________________________

Speaker: Jacek Mandziuk

Affiliation: International Computer Science Institute, Berkeley
and EECS Department UC Berkeley

e-mail: mandziuk@icsi.berkeley.edu

Title: Incremental Class Learning approach and its application
to Handwritten Digit Recognition

Date: June 23, 1997
Location: 320 Soda Hall, University of California, Berkeley, CA

Abstract:

Incremental Class Learning (ICL) provides a feasible framework for development
of scalable learning systems. Instead of learning the whole problem at once,
ICL focuses on learning subproblems incrementally, one at a time, using the
results of prior learning during subsequent learning, and then combining the
solutions in an appropriate manner.
Therefore, the cathastrophic interference problem'', which occurs
in sequential learning, is significantly alleviated.

The ICL approach presented in this talk can be summarized as follows.
Primarily the system focuses on one category and after it learns this
category, it tries to identify the compact subset of features (nodes) in the
hidden layers, that are crucial for recognition of this category.
The system then {\em freezes} these crucial nodes (features) by fixing their
incoming weights. As a results, they cannot be obliterated in subsequent
learning. These frozen features are availiable for learning other categories,
and serve as parts of weight structures build subsequently to recognize other
categories. The set of features gradually stabilizes and eventually,
learning a new category requires less effort, and primarily
consists of combining existing features in an appropriate manner.
Eventually a large number of nodes are {\em shared} among various categories.

We present results of applying the ICL approach to the Handwritten Digit
Recognition problem, based on the Spatio-Temporal representation of patterns.

Joint work with Lokendra Shastri.