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Learning functions using randomized genetic code-like transformations: Probabilistic properties and experimentations
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DOI：10.1109/TKDE.2004.27.png)
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Inductive learning of nonlinear functions plays an important role in constructing predictive models and classifiers from data. This paper explores a novel randomized approach to construct linear representations of nonlinear functions proposed elsewhere [ 14], [17]. This approach makes use of randomized codebooks, called the Genetic Code-Like Transformations (GCTs) for constructing an approximately linear representation of a nonlinear target function. This paper first derives some of the results presented elsewhere [ 17] in a more general context. Next, it investigates different probabilistic and limit properties of GCTs. It also presents several experimental results to demonstrate the potential of this approach.
Keyword:
inductive function learning
genetic code-like transformations
representation construction
randomized transformations
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