No abstract

Machine‐learning algorithms have performed well on noisy datasets that are typical of financial data. This paper compares the performance of three types of machine‐learning classifier for selecting money managers. Naïve Bayes, neural network and decision tree learners were applied to a dataset of US equity managers. Although other studies have suggested that the performance of classifiers appears to be highly dependent on the nature of the problem and the dataset, the learning algorithms each had similar predictive accuracy and all outperformed by a substantial margin simple manager selection rules that are typical of the ways in which money managers and mutual funds are selected by investors. The results indicate that machine learners can be used as a decision‐support aid to improve the selection of money managers. Copyright © 2005 John Wiley & Sons, Ltd.

Real-time imaging applications are concerned with efficient and deterministic processing of digital images. These applications are predominantly written using structured programming rather than object-oriented programming with the belief that the former approach has better performance characteristics. Current research shows that this may not be the case and an object-oriented approach may not only result in efficient code but one that is more maintainable and understandable. We look at current techniques for visualizing the code for software applications to determine if they can help predict its qualities such as maintainability, understandability and performance, and suggest ways in which these techniques can be enhanced to meet the specific needs of real-time imaging applications.

No abstract

No abstract