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Learning Functions Using Randomized Genetic Code-Like Transformations: Probabilistic Properties and Experimentations
August 2004 (vol. 16 no. 8)
pp. 894-908

Abstract—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 [CHECK END OF SENTENCE], [CHECK END OF SENTENCE]. 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 [CHECK END OF SENTENCE] 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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Index Terms:
Inductive function learning, genetic code-like transformations, representation construction, randomized transformations.
Citation:
Hillol Kargupta, Rajeev Ayyagari, Samiran Ghosh, "Learning Functions Using Randomized Genetic Code-Like Transformations: Probabilistic Properties and Experimentations," IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 8, pp. 894-908, Aug. 2004, doi:10.1109/TKDE.2004.27
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