XNN


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AcronymDefinition
XNNXena News Network
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XNNThD also shows quite excellent performance and we should note that XNN is feature-based model.
It is well known that feature-based models have an advantage over lazy classification models such as XNN in efficiency.
XNn 336,592 87,362 1.4 1.4 junta 983 0 6,5 -- intervencia 1,612 0 6,4 -- prokuratura 2,609 0 6,2 -- kontrar ozviedka 841 0 6,2 -- diktatura 976 0 6,0 -- utvar 2,414 0 5,9 -- hodnost' 1,028 0 5,8 -- rozviedka 662 0 5,8 -- zakladna 5,784 23 6,8 -0,9 prevrat 1,249 11 6,1 0,1 uniforma 1,149 12 5,8 -0,2 velitel' 2,157 52 5,8 0,7 spravodaj stvo 3,152 70 6,8 1,8 letectvo 2,217 65 6,9 2,5 operacia 5,797 400 6,1 2,3 t'azenie 900 486 5,8 5,9 namornictvo 1,036 566 6,3 6,6 cintorin 2,539 3,024 5,7 6,3 konflikt 3,055 5,552 5,5 6,5 masineria 289 264 4,5 5,7 lod'stvo 144 299 3,6 6,2 veteran 513 2,679 4,6 7,6 invalid 49 306 1,9 5,7 zajatec 250 2,341 4,0 8,2 korist' 49 666 1,5 6,1 zlocin 97 5,453 1,2 7,3 sekera 18 766 -0,1 6,0 zlocinec 21 3,385 0,0 8,0 reparada reparacie 0 227 -- 6,2 stvac 0 426 -- 7,2
Considering the special case where we assume zero cost for a correct classification, that is, XPP = XNN = 0, the decision costs of rules can be simply expressed as follows:
As illustrated in Figure 2, a XNN hypersphere, formed by the K's nearest neighboring (XNN) points of [X.sub.i], is a cloud composed of Km-dimensional neighboring points around [X.sub.i].
Another point cloud around [X.sub.i] is formed with respect to its mutual neighbors [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], which share the same temporal indexes of the XNN of [Y.sub.i] In this sense, the Y-conditioned average square Euclidean distance is defined by replacing the true nearest neighbors of [X.sub.i] by the mutual neighbors [37]:
193) lists "nose": 'nf, xsm, nxr, xnfr, xnn, xtm, txm, nngrt, njrg, p/buz.
Because there were no discernible differences between these control plants and wild-type plants, the control plants were labeled as Xnn equivalent to wild-type plants (N.
Leaf water potential (LWP), relative water content (RWC), total leaf area, and aboveground biomass of APX3-overexpression plants (Y3 and B3) and control plants (Xnn) under water-deficit treatments.
To develop the model, three steps would be introduced, first, by analyzing the traffic spatial and temporal characteristic proposed nonparametric regression (NPR) and k nearest neighbor (XNN) method.