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We parameterized the Guided Search model [Wolfe, J.M., 1994. Guided Search 2.0: a revised model of visual search. Psychonomic Bulletin and Review 1(202 238]238] for a task involving visual search of a target store in a map.
In this paper we examine the case when the DRP fails, and provide an analytical model of search complexity parameterized by the probability of an abstract solution being refinable.
The authors show a multikernel SVM with automatic parameterization to improve the results of SVM parameterized under a brute force search.
Searches were parameterized with a cut-off E-value of <10−5.
The procedure was parameterized by allowing a free search of the thickness of the upper layer and a search within 50% of the input V s value.
This paper shows a multikernel SVM to manage highly dimensional data, providing an automatic parameterization with low computational cost and improving results against SVMs parameterized under a brute-force search.
In this paper, we show a multikernel SVM to manage highly dimensional data, providing an automatic parameterization with low computational cost and improving results against SVMs parameterized under a brute-force search.
In a nutshell, instead of searching for a general nonnegative function, we can constrain our search to functions that can be parameterized as sums of squares of polynomials.
Formally, the strategy can be represented as a tree, where the leaves are searches, and internal nodes are parameterized operations that combine or transform results.
We describe a novel parameterized space of double auctions, and then introduce an evolutionary search method that searches this space of parameters.
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