the worst (Zaire) to the best (US or New Zealand) institutions in our sample would be associated with a five fold increase in income per capita.

Abstract. Methods for voting classification algorithms, such as Bagging and AdaBoost, have been shown to be very successful in improving the accuracy of certain classifiers for artificial and real-world datasets. We review these algorithms and describe a large empirical study comparing several

retransmit events. Our measurements demonstrate that our model is able to more accurately predict TCP throughput and is accurate over a wider range of loss rates. This material is based upon work supported by the National Science Foundation under grants NCR-95-08274, NCR-95-23807 and CDA-95-02639. Any

to work well. In this paper we investigate loopy prop agation empirically under a wider range of conditions. Is there something special about the error-correcting code setting, or does loopy propagation work as an approximation scheme for a wider range of networks? ..\ x(:x).) (1) where: and: The message

Machine learning for text classification is the cornerstone of document categorization, news filtering, document routing, and personalization. In text domains, effective feature selection is essential to make the learning task efficient and more accurate. This paper presents an empirical comparison

of the geographic areas for which data are available. As a conse-quence, comparisons of the degree of geographic concentration across industries can be made with more confidence. Our empiri-cal results provide a strong reaffirmation of the previous wisdom in that we find almost all industries to be somewhat

for this state of affairs is that existing theories of work The authors express great appreciation to members of the consulting firm that helped us gain access to the organizations where this research was conducted; to Kenneth Brousseau, Daniel Feldman, and Linda Frank for assistance in data collection

This paper presents a critical appraisal of resilience, a construct connoting the maintenance of positive adaptation by individuals despite experiences of significant adversity. As empirical research on resilience has burgeoned in recent years, criticisms have been levied at work in this area

made on these topics in both empirical and theoretical work in machine learning, and we present a general framework that we use to compare different methods. We close with some challenges for future work in this area.

Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field