Integrative Windowing

In this paper we re-investigate windowing for rule learning algorithms. We show that, contrary to previous results for decision tree learning, windowing can in fact achieve significant run-time gains in noise-free domains and explain the different behavior of rule learning algorithms by the fact that they learn each rule independently. The main contribution of this paper is integrative windowing, a new type of algorithm that further exploits this property by integrating good rules into the final theory right after they have been discovered. Thus it avoids re-learning these rules in subsequent iterations of the windowing process. Experimental evidence in a variety of noise-free domains shows that integrative windowing can in fact achieve substantial run-time gains. Furthermore, we discuss the problem of noise in windowing and present an algorithm that is able to achieve run-time gains in a set of experiments in a simple domain with artificial noise.Comment: See http://www.jair.org/ for any accompanying file

MENUING SOFTWARE: APPLICATION TO THE GENERAL MANAGER'S NEED

This paper demonstrates a robust approach to general manager computer use. The approach is shown to systematically integrate a diverse collection of user selected applications. It is implemented through the use of currently available shareware and public domain software at low cost. The developed prototype follows accepted psychological choice, Management Information System (MIS) and/or Decision Support System (DDS) principles. It is easily modified by a user, said to be any general management team member, as the environment changes. Team member roles, concerns, styles, and interests can be accommodated in the design or re-design. The prototype and approach is friendly to occasional users. One basic installation can serve several users. It will work on a network of computers. It offers a sense of staying in control to the individual. Complexities of application choice and access are hidden. Data transfer between applications is automated. Extension educators and business consultants can also use a similar approach for accessing a wide variety of applications. Further work will likely improve the basic approach. In the meantime, the gain from using this approach as it stands is quickly available.Research Methods/ Statistical Methods,

Propofol Induction Reduces the Capacity for Neural Information Integration: Implications for the Mechanism of Consciousness and General Anesthesia

The cognitive unbinding paradigm suggests that the synthesis of cognitive information is attenuated by general anesthesia. Here, we investigated the functional organization of brain activities in the conscious and anesthetized states, based on characteristic functional segregation and integration of electroencephalography (EEG). EEG recordings were obtained from 14 subjects undergoing induction of general anesthesia with propofol. We quantified changes in mean information integration capacity in each band of the EEG. After induction with propofol, mean information integration capacity was reduced most prominently in the gamma band of the EEG (p=0.0001). Furthermore, we demonstrate that loss of consciousness is reflected by the breakdown of the spatiotemporal organization of gamma waves. Induction of general anesthesia with propofol reduces the capacity for information integration in the brain. These data directly support the information integration theory of consciousness and the cognitive unbinding paradigm of general anesthesia

Scaling up classification rule induction through parallel processing

The fast increase in the size and number of databases demands data mining approaches that are scalable to large amounts of data. This has led to the exploration of parallel computing technologies in order to perform data mining tasks concurrently using several processors. Parallelization seems to be a natural and cost-effective way to scale up data mining technologies. One of the most important of these data mining technologies is the classification of newly recorded data. This paper surveys advances in parallelization in the field of classification rule induction

Periodicity in wide-band time series

Summary: To test the hypotheses that (i) electroencephalograms (EEGs) are largely made up of oscillations at many frequencies and (ii) that the peaks in the power spectra represent oscillations, we applied a new method, called the Period Specific Average (PSA) to a wide sample of EEGs. Both hypotheses can be rejected