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Feature selection has become an important research area in machine learning due to rapid advances in technology. In high-dimensional spaces, the difficulty of classification is intrinsically caused by the existence of irrelevant and redundant features that, in general, degrade the performance of a classifier. Moreover, finding the optimal subset of features becomes intractable even for low-dimensional...
In classification tasks, as the dimensionality increases, the performance of the classifier improves until an optimal number of features is reached. Further increases of the dimensionality without increasing the number of training samples results in a degradation in classifier performance. This fact, called the curse of dimensionality, has become more relevant with the advent of larger datasets and...
It is believed that meteorological factors such as rain, wind or temperature do not affect significantly to wireless systems operating at radio frequencies below 10 GHz. However, using an appropriate statistical analysis to the measurements taken from an outdoor IEEE 802.11b/g WLAN, we show that this is not true. Although these networks operate at 2.4 GHz, we have been able to prove a significant...
Wireless Area Networks are commonly used for a great variety of applications. Actually, more and more people are using them as the access network technology for their home network or to access to Internet through a wireless router. In many cases, the access is performed in outdoors, not only in indoors. On the other hand, networks are experiencing a huge demand of services such as VoIP and IPTV that...
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