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A graph model is presented to analyze the performance of a relational join. The amount of page reaccesses, the page access sequence, and the amount of buffer needed are represented in terms of graph parameters. By using the graph model formed from the index on the join attributes, we determine the relationships between these parameters. Two types of buffer(More)
23 and that two defects (blurring and thresholding) aaect classiication accuracy signiicantly, continuously, and monotonically. We believe that these results, as well as the methodology, can be further exploited to guide the training of individual classiiers and coordination of multiple classiiers. We have focused our attention on methodologies for making(More)
17 a similar reason, we did not nd the method of projection pursuit 5] in exploratory data analysis useful in this context. 4 Conclusions We described a method for inferring from the training data faithful but concise representations of the empirical class-conditional distributions. In doing this, we have abandoned many usual simplifying assumptions about(More)
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