Many problems in computation can be specified in terms of computing the inverseof an easily constructed function. However, studies on how to derive an algorithm froma problem specification involving inverse functions are relatively rare. The aim of thisthesis is to demonstrate, in an example-driven style, a number of techniques to do the job.The techniques are based on the frame...

The basic Schema Theorem for genetic algorithms is modiied for a grammatically-based learning system. A context-free grammar is used to deene a language in which each sentence is mapped to a tness value. The derivation trees associated with these sentences are used to deene the structure of schemata. The eeect of crossover and mutation on schemata is described. A schema theorem is developed whi...

We describe a machine-learning system that uses linear vector-space based techniques for inference from observations to extend previous work on model construction for particle physics [10, 9, 5]. The program searches for quantities conserved in all reactions from a given input set; given data based on frequent decays it rediscovers the family conservation laws: baryon#, electron#, muon# and tau...

Generation mean analysis was carried out to estimate the nature andmagnitude of gene effects for sugar yield and its component traits in sweet sorghum[Sorghum bicolor (L.) Moench. Six basic generations, namely P1, P2, F1, F2,BC1P1, BC1P2 of four crosses involving seven diverse parents were evaluated inrainy 2009. The mean performance of the F1 in all the crosses indicated dominantgene effect fo...

We show that  higher derivations on a Hilbert$C^{*}-$module associated with the Cauchy functional equation satisfying generalized Hyers--Ulam stability.  

In many scientific studies, the response variable bears a generalized nonlinear regression relationship with a certain covariate of interest, which may, however, be confounded by other covariates with unknown functional form. We propose a new class of models, the partly parametric generalized additive model (PPGAM) for doing generalized nonlinear regression with the confounding covariate effect...

We provide an explicit rigorous derivation of a diffusion limit – a stochastic differential equation with additive noise – from a deterministic skewproduct flow. This flow is assumed to exhibit time-scale separation and has the form of a slowly evolving system driven by a fast chaotic flow. Under mild assumptions on the fast flow, we prove convergence to a stochastic differential equation as th...

Sigmoidal or radial transfer functions do not guarantee the best generalization nor fast learning of neural networks. Families of parameterized transfer functions provide flexible decision borders. Networks based on such transfer functions should be small and accurate. Several possibilities of using transfer functions of different types in neural models are discussed, including enhancement of i...

Let S be a ring with involution having nontrivial symmetric idempotent element id="M3"> e . If id="M4"> Ω is any appropriate multiplicative generalized reverse id="M5"> ∗ CE-derivation of id="M6"> id="M7"> , then under some suitable restricti...