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This paper presents a quantitative complexity theory for human-machine interaction and validates the developed theory through experiments. Based on work in basic research an information-theoretic complexity measure C is introduced The main advantage of the novel measure is that it solely relies on information theoretic quantities and is independent from possibly unreliable subjective ratings or psychophysiological...
This paper deals with an assumption of the Shannon theory, notably we analyze the possibility of counting n 'selected' items which is prerequisite to the entropy calculus. We start from the notion, which Shannon provides verbally, we translate it into a formal definition and calculate three cases regarding both analog and digital signals. In such a way we aim at sharpening the applications up and...
Data partitioning, such as bagging and boosting, has been extensively used in the construction of multiple classifier systems. One objective of data partitioning is to achieve uncorrelated classifiers. Most existing techniques achieve diversity through random partitioning, and they do not take advantage of the information within data patterns before training. In this work, combining techniques are...
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