Optimization algorithms for energy-efficient data centers
Hendrik F. Hamann
InterPACK 2013
Generating classification rules or decision trees from examples has been a subject of intense study in the pattern recognition community, the statistics community, and the machine-learning community of the artificial intelligence area. We pursue a point of view that minimality of rules is important, perhaps above all other considerations (biases) that come into play in generating rules. We present a new minimal rule-generation algorithm called R-MINI (Rule-MINI) that is an adaptation of a well-established heuristic-switching-function-minimization technique, MINI. The main mechanism that reduces the number of rules is repeated application of generalization and specialization operations to the rule set while maintaining completeness and consistency. R-MINI results on some benchmark cases are also presented. © 1997 IEEE.
Hendrik F. Hamann
InterPACK 2013
Robert G. Farrell, Catalina M. Danis, et al.
RecSys 2012
Elizabeth A. Sholler, Frederick M. Meyer, et al.
SPIE AeroSense 1997
David S. Kung
DAC 1998