Title:
Compositional Classification
Compositional Classification
Author(s)
Jones, Joshua
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Abstract
An intelligent system must routinely deal with massive information processing
complexity. The research discussed in this document is concerned with finding
representations and processes to deal with a part of this complexity. At a
high level, the proposed idea is that a synthesis between the symbolic reasoning
of classic artificial intelligence research and the statistical inference mechanisms
of machine learning provides answers to some of these issues of complexity. This
research is specifically concerned with a subset of classification problems that we
call ”compositional classification”, where both the class label and values produced
at internal nodes in the classification structure entail verifiable predictions. This
research specifies and evaluates a technique for compositional classification. This
investigation will consist of (i) implementing a framework for the construction of
supervised classification learning systems that codifies the technique, (ii) instantiating
a number of learning systems for various specific classification problems
using the framework, (iii) using a synthetic problem setting to systematically vary
the problem characteristics and system parameters and assess the impact on performance,
and (iv) formally analyzing the properties of the technique. A central
problem addressed by this technique is how diverse techniques for representation,
reasoning and learning that arise from differing viewpoints on intelligence can be
reconciled to form a consistent and effective whole. For example, how can neural
network backpropagation and knowledge-based diagnosis be combined to achieve
an effective structural credit assignment technique for a hybrid knowledge representation?
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Date Issued
2008-03-25
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Resource Type
Text
Resource Subtype
Technical Report