Paper announcements

[Nathan Intrator]

                   *** Papers Announcements ***
 
The following  papers are now available from my research page:
http://www.physics.brown.edu/people/nin/research.html
 
Comments are welcomed.
 
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Classifying Seismic Signals by Integrating Ensembles of Neural Networks

              Yair Shimshoni and Nathan Intrator
 	 ftp://cns.brown.edu/nin/papers/hong-kong.ps.Z

This paper proposes a classification scheme based on the
integration of multiple Ensembles of ANNs. It is demonstrated on
a classification problem, in which Seismic recordings of Natural
Earthquakes must be distinguished from the recordings of 
Artificial Explosions.  A Redundant Classification Environment
consists of several Ensembles of Neural Networks is created and
trained on Bootstrap Sample Sets, using various data representations
and architectures. The ANNs within the Ensembles are aggregated
(as in Bagging) while the Ensembles are integrated non-linearly, in a
signal adaptive manner, using a posterior confidence measure
based on the agreement (variance) within the Ensembles.  The proposed
Integrated Classification Machine achieved 92.1\% correct
classification on the seismic test data. Cross Validation evaluations
and comparisons indicate that such integration of a collection of 
ANN's Ensembles is a robust way for handling high dimensional
problems with a complex non-stationary signal space as in the current
Seismic Classification problem.

To appear: Proceedings of ICONIP 96

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	Learning low dimensional representations of visual objects 
	         with extensive use of prior knowledge

                  Nathan Intrator and Shimon Edelman 
                ftp://cns.brown.edu/nin/papers/ml1.ps.Z

  Learning to recognize visual objects from examples requires the
  ability to find meaningful patterns in spaces of very high
  dimensionality.  We present a method for dimensionality reduction
  which effectively biases the learning system by combining multiple
  constraints via an extensive use of class labels.  The use of
  multiple class labels steers the resulting low-dimensional
  representation to become invariant to those directions of variation
  in the input space that are irrelevant to classification; this is
  done merely by making class labels independent of these directions.
  We also show that prior knowledge of the proper dimensionality of
  the target representation can be imposed by training a
  multiple-layer bottleneck network. A series of computational
  experiments involving parameterized fractal images and real human
  faces indicate that the low-dimensional representation extracted by
  our method leads to improved generalization in the learned tasks,
  and is likely to preserve the topology of the original space.

To appear: EXPLANATION-BASED NEURAL NETWORK LEARNING: A LIFELONG LEARNING
	APPROACH. Editor: SEBASTIAN THRUN

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     Bootstrapping with Noise: An Effective Regularization Technique

                   Yuval Raviv and Nathan Intrator
             ftp://cns.brown.edu/nin/papers/spiral.ps.Z

Bootstrap samples with noise are shown to be an effective smoothness
and capacity control technique for training feed-forward networks and
for other statistical methods such as generalized additive models. 
It is shown that noisy bootstrap performs best in  conjunction with 
weight decay regularization and ensemble averaging.
The two-spiral problem, a highly non-linear noise-free data, is used to
demonstrate these findings.
The combination of noisy bootstrap and ensemble averaging is also
shown useful for generalized additive modeling, and is also
demonstrated on the well known Cleveland Heart Data
\cite{Detrano89}.

To appear: Connection Science, Speciall issue on Combining Estimators.