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Learning functions using randomized genetic code-like transformations: probabilistic properties and experimentations

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3 Author(s)
Kargupta, H. ; Dept. of Comput. Sci. & Electr. Eng., Maryland Univ., Baltimore, MD, USA ; Ayyagari, R. ; Ghosh, S.

Inductive learning of nonlinear functions plays an important role in constructing predictive models and classifiers from data. We explore a novel randomized approach to construct linear representations of nonlinear functions proposed elsewhere [H. Kargupta (2001)], [H. Kargupta et al., (2002)]. 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. We first derive some of the results presented elsewhere [H. Kargupta et al., (2002)] 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.

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Knowledge and Data Engineering, IEEE Transactions on  (Volume:16 ,  Issue: 8 )