By Narayanan A., Keedwell E., Savic D.
It truly is an open query as to what's the right way to extract symbolic principles from proficient neural networks in domain names related to category. earlier ways in accordance with an exhaustive research of community connection and output values have already been established to be intractable in that the scale-up issue raises exponentially with the variety of nodes and connections within the community. a singular process utilizing genetic algorithms to look for symbolic principles in a informed neural community is verified during this paper. initial experiments related to category are said the following, with the implications indicating that our proposed technique is profitable in extracting ideas. whereas it truly is authorized that extra paintings is needed to convincingly reveal the prevalence of our process over others, there's however enough novelty in those effects to justify early dissemination. (If the paper is approved, the most recent effects can be said, including enough info to help replicability and verification.)
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Extra info for Data mining neural networks with genetic algorithms
The results for the data from Table 2 are given in Table 6. AMOUNT OF INFORMATION CONTAINED IN p , J^, s, g^, AND g^ 25. , Xn, of some quality characteristic, how can we present concisely information by means of which the observed distribution can be closely approximated, that is, so that the percentage of the total number, n, of observations lying within any stated interval from, say, X-atoX = b, can be approximated? the quantities X, s^, gi and g2, as Table 6. 797 Oz/ft2 Breaking Strength, lb 27 PRESENTATION OF DATA The total information can be presented only by giving all of the observed values.
1925, pp. 364-387.  Hoel, P. , Wiley, New York, 1984.  Lewis, C. , Mind and Scribner, New York, 1929. the  Keynes, J. , A Treatise MacMillan, New York, 1921. World on Order, Probability, 4. Present as much evidence as possible that the data were obtained under controlled conditions.  Dodge, H. , "Statistical Control in Sampling Inspection," presented at a round table discussion on "Acquisition of Good Data," held at the 1932 Annual Meeting of the American Society for Testing and Materials; published in American Machinist, 26 Oct.
If we are interested in the percentages of the total number of observations that have values above (or below) several values on the scale of measurement, the essential information may be contained in a tabular grouped frequency distribution plus a statement of the number of observations n. But even here, if n is large and if the data represent controlled conditions, the essential information may be contained in the four sample functions—the average X, the standard deviation s, the skewness g^, the kurtosis g^, and the number of observations n.