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» Self-organizing maps and symbolic data
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HYBRID
1998
Springer
14 years 4 days ago
High Order Eigentensors as Symbolic Rules in Competitive Learning
We discuss properties of high order neurons in competitive learning. In such neurons, geometric shapes replace the role of classic `point' neurons in neural networks. Complex ...
Hod Lipson, Hava T. Siegelmann
JMLR
2010
230views more  JMLR 2010»
13 years 2 months ago
Learning Dissimilarities for Categorical Symbols
In this paper we learn a dissimilarity measure for categorical data, for effective classification of the data points. Each categorical feature (with values taken from a finite set...
Jierui Xie, Boleslaw K. Szymanski, Mohammed J. Zak...
JOT
2008
136views more  JOT 2008»
13 years 7 months ago
The Stock Statistics Parser
This paper describes how use the HTMLEditorKit to perform web data mining on stock statistics for listed firms. Our focus is on making use of the web to get information about comp...
Douglas Lyon
PASTE
2005
ACM
14 years 1 months ago
Generalizing symbolic execution to library classes
Forward symbolic execution is a program analysis technique that allows using symbolic inputs to explore program executions. The traditional applications of this technique have foc...
Sarfraz Khurshid, Yuk Lai Suen
GPCE
2005
Springer
14 years 1 months ago
Mapping Features to Models: A Template Approach Based on Superimposed Variants
Although a feature model can represent commonalities and variabilities in a very concise taxonomic form, features in a feature model are merely symbols. Mapping features to other m...
Krzysztof Czarnecki, Michal Antkiewicz