In this paper, we propose techniques to improve the efficiency of systems acting with knowledge bases and based on functional neural networks (FN-networks) formalism due to the use of their structural features. The ar...
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In this paper, we propose techniques to improve the efficiency of systems acting with knowledge bases and based on functional neural networks (FN-networks) formalism due to the use of their structural features. The article addresses the case of partially defined FN-networks, which have a regular structure and are set as lists of multiple similar objects and fragments with common structure such that their number is unknown and may be unlimited. It offers an algorithm to find solution, which is based on dynamic formation of limited, fully determined local fragments of partially defined network and subsequent transfer of the results to the entire FN-network, which may be endless.
In this paper we consider the problem of estimating the relative performance of a given set of related algorithms. The predominant, general approach of doing so involves executing each algorithm instance multiple time...
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ISBN:
(纸本)9783319694047;9783319694030
In this paper we consider the problem of estimating the relative performance of a given set of related algorithms. The predominant, general approach of doing so involves executing each algorithm instance multiple times, and computing independent estimates based on the performance observations made for each of them. A single execution might be expensive, making this a time-consuming process. We show how an algorithm in general can be viewed as a distribution over executions;and its performance as the expectation of some measure of desirability of an execution, over this distribution. Subsequently, we describe how Importance Sampling can be used to generalize performance observations across algorithms with partially overlapping distributions, amortizing the cost of obtaining them. Finally, we implement the proposed approach as a Proof of Concept and validate it experimentally.
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