We address the problem of nding the parameter settings that will result in optimal performance of a given learning algorithm using a particular dataset as training data. We describe a \wrapper" method, considering determination of the best parameters as a discrete function optimization problem. The method uses bestrst search and crossvalidation to wrap around the basic induction algorithm: the ...

Stack Filters define a large class of discrete nonlinear filter first introduced in image and signal processing for noise removal. In recent years we have suggested their application to classification problems, and investigated their relationship to other types of discrete classifiers such as Decision Trees. In this paper we focus on a continuous domain version of Stack Filter Classifiers which...

I. Abstract It is well-established that a multi-layer perceptron (MLP) with a single hidden layer of N neurons and an activation function bounded by zero at negative infinity and one at infinity can learn N distinct training sets with zero error. Previous work has shown that the input weights and biases for such a MLP can be chosen in an effectively arbitrary manner; however, this work makes th...

We address the problem of nding the parameter settings that will result in optimal performance of a given learning algorithm using a particular dataset as training data. We describe a \wrapper" method, considering determination of the best parameters as a discrete function optimization problem. The method uses best-rst search and cross-validation to wrap around the basic induction algorithm: th...

The optimum receive antenna selection combining rule that minimizes the bit error probability is presented. It is derived from a general relationship between the bit error probability and the log-likelihood ratio (LLR), and selects the receive antenna providing the largest LLR magnitude. The optimum selection combining rule is applied to single transmit (Tx) antenna and space-time block code (S...

This paper gives a widely applicable technique for solving many of the parameter estimation problems encountered in geometric computer vision. A commonly used approach is to minimize an algebraic error function instead of a possibly preferable geometric error function. It is claimed in this paper that minimizing algebraic error will usually give excellent results, and in fact the main problem w...

We develop a forecasting methodology for providing credible forecasts time series that have recently undergone shock. achieve this by borrowing knowledge from other similar shocks which post-shock outcomes are observed. Three shock effect estimators motivated with the aim of minimizing average forecast risk. propose risk-reduction propositions provide conditions establish when our works. Bootst...