Learning decision trees using the Fourier spectrum
STOC '91 Proceedings of the twenty-third annual ACM symposium on Theory of computing
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
The nature of statistical learning theory
The nature of statistical learning theory
The harmonic sieve: a novel application of Fourier analysis to machine learning theory and practice
The harmonic sieve: a novel application of Fourier analysis to machine learning theory and practice
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Toward Machine Learning Through Genetic Code-like Transformations
Genetic Programming and Evolvable Machines
Machine Learning
The behavior of adaptive systems which employ genetic and correlation algorithms
The behavior of adaptive systems which employ genetic and correlation algorithms
Knowledge discovery from heterogeneous data streams using fourier spectrum of decision trees
Knowledge discovery from heterogeneous data streams using fourier spectrum of decision trees
Perceptrons: An Introduction to Computational Geometry
Perceptrons: An Introduction to Computational Geometry
Toward Machine Learning Through Genetic Code-like Transformations
Genetic Programming and Evolvable Machines
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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 [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.