In the previous lecture we defined the principle of Maximum Likelihood (ML): suppose we have random variables X1, ..., Xn form a random sample from a discrete distribution whose joint probability distribution is P (x | φ) where x = (x1, ..., xn) is a vector in the sample and φ is a parameter from some parameter space (which could be a discrete set of values — say class membership). When P (x | ...

An important consideration when applying neural networks is the sensitivity to weights and threshold in strict separating systems representing a linearly separable function. Two parameters have been introduced to measure the relative errors in weights and threshold of strict separating systems: the tolerance and the greatest tolerance. Given an arbitrary separating system we study which is the ...

in this article we consider relative iteration of entire functions and studycomparative growth of the maximum term of iterated entire functions withthat of the maximum term of the related functions.