A tale of two classifier systems

This paper describes two classifier systems that learn. These are rule-based systems that use genetic algorithms, which are based on an analogy with natural selection and genetics, as their principal learning mechanism, and an economic model as their principal mechanism for apportioning credit. CFS-C is a domain-independent learning system that has been widely tested on serial computers. * CFS is a parallel implementation of CFS-C that makes full use of the inherent parallelism of classifier systems and genetic algorithms, and that allows the exploration of large-scale tasks that were formerly impractical. As with other approaches to learning, classifier systems in their current form work well for moderately-sized tasks but break down for larger tasks. In order to shed light on this issue, we present several empirical studies of known issues in classifier systems, including the effects of population size, the actual contribution of genetic algorithms, the use of rule chaining in solving higher-order tasks, and issues of task representation and dynamic population convergence. We conclude with a discussion of some major unresolved issues in learning classifier systems and some possible approaches to making them more effective on complex tasks.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46937/1/10994_2004_Article_BF00113895.pd

American College of Cardiology; American Heart Association Task Force; European Society of Cardiology Committee for Practice Guidelines. ACC/AHA/ESC 2006 guidelines for management of patients with ventricular arrhythmias and the prevention of sudden cardiac death: a report of the American College of Cardiology/American Heart Association Task Force and the European Society of Cardiology Committee for Practice Guidelines (Writing Committee to Develop Guidelines for Management of Patients With Ventricular Arrhythmias and the Prevention of Sudden Cardiac Death).

The purpose this document is to update and combine the previously published recommendations into one source approved by the major cardiology organizations in the United States and Europe. We have consciously attempted to create a streamlined document, not a textbook, that would be useful specifically to locate recommendations on the evaluation and treatment of patients who have or may be at risk for ventricular arrhythmias. Thus, sections on epidemiology, mechanisms and substrates, and clinical presentations are brief, because there are no recommendations for those sections. For the other sections, the wording has been kept to a minimum, and clinical presentations have been confined to those aspects relevant to forming recommendations