We consider exact attribute-efficient learning of functions from Post closed classes using membership queries and obtain bounds on learning complexity.
We consider exact attribute-efficient learning of functions from Post closed classes using membership queries and obtain bounds on learning complexity.
This paper presents new upper bounds for binary covering arrays of variable strength constructed by using a new Simulated Annealing (SA) algorithm. This algorithm incorporates several distinguished features including ...
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(纸本)9781424481262
This paper presents new upper bounds for binary covering arrays of variable strength constructed by using a new Simulated Annealing (SA) algorithm. This algorithm incorporates several distinguished features including an efficient heuristic to generate good quality initial solutions, a compound neighborhood function which combines two carefully designed neighborhoods and a fine-tuned cooling schedule. Its performance is investigated through extensive experimentation over well known benchmarks and compared with other state-of-the-art algorithms, showing that the proposed SA algorithm is able to outperform them.
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