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Table 4. Subfields in a feature layer

by unknown authors 1994

"... In PAGE 5: ...X2I si = Xj X1sjW1sji; X2II si 0 = f(X1I si=(a+Xj X2I sj)); X2si = X2II si =(a+Xj X2II sj ) f(x) = 2=(1 + exp(?Tx)) ? 1 where s is a suffix which is either A, B, or C and T is the steepness of the sigmoid function and a is a small positive constant. Table4 shows the meaning of above suffix i. Category Layer X3pi = 8 lt; : 1 if i = minfjj Pks X2skW2skj gt; g; or if = minfjj Pks X2skW2skj gt; g amp; unrefi =max j funrefjg 0 otherwise (2) Where is the least match score required and urefi is an unreferenced count.... ..."

Cited by 2

Table 1: Systems features for the layers of abstraction of products.

in A Systems Model for the Field of Informatics A. J. Cowling Abstract A Systems Model for the Field of Informatics

by A. J. Cowling

Table 2: Systems features for the layers of abstraction of processes.

in A Systems Model for the Field of Informatics A. J. Cowling Abstract A Systems Model for the Field of Informatics

by A. J. Cowling

Table 2-3 Pros and Cons of MAC layer features

in ACKNOWLEDGEMENTS

by Richard Murphy Ba 2004

"... In PAGE 8: ...viii LIST OF TABLES Table2 -1 Comparison of DOCSIS 1.... In PAGE 8: ...able 2-1 Comparison of DOCSIS 1.1 to Ethernet.......................................................................15 Table2 -2 Comparison of DOCSIS 1.... In PAGE 8: ...able 2-2 Comparison of DOCSIS 1.1 to IEEE 802.14................................................................16 Table2 -3 Pros and Cons of MAC layer features .... In PAGE 23: ... In addition, the implementation of DOCSIS is complicated by the open-ended nature of MAP generation. Table2 -1 condenses the comparison of DOCSIS 1.1 and Ethernet.... In PAGE 24: ... Table2 -1 Comparison of DOCSIS 1.1 to Ethernet Feature Ethernet (10/100 Base X) DOCSIS 1.... In PAGE 24: ... Most of this prior work has involved comparisons of the relative efficiency of each collision avoidance algorithm. Table2 -2 gives four key differences ... In PAGE 25: ... Table2 -2 Comparison of DOCSIS 1.1 to IEEE 802.... ..."

Table 1: Connections between the feature map layer and subsampling map layer in the LeCun architecture.

in Nonlinear Image Processing Using Artificial Neural Networks

by Dick de Ridder, Robert P.W. Duin, Michael Egmont-petersen, Lucas J. van Vliet, Piet W. Verbeek

Table 1. Advantages of FeatureC++ Aspectual Mixin Layers.

in FeatureC : On the Symbiosis of Feature-Oriented and Aspect-Oriented Programming

by Sven Apel, Thomas Leich, Marko Rosenmüller, Gunter Saake 2005

"... In PAGE 12: ... The implementation is straight- forward and was described many times. Table1 summarizes the contribution of Aspectual Mixin Layers. We readily admit that this simple case study cannot prove our ideas, and we do not intend to do so.... ..."

Cited by 8

Table 2. Advantages of FeatureC++ Aspectual Mixin Layers.

in FeatureC++: Feature-Oriented and Aspect-Oriented Programming in C++

by Sven Apel, Thomas Leich, Marko Rosenmüller, Gunter Saake

"... In PAGE 17: ... Furthermore, it is able to handle dynamic crosscutting, interface extensions, non-hierarchy-conform refinements, and excessive method shadowing. Table2 summarizes these advantages. 7 Related Work Work in several areas is related to this contribution: programming support for FOP, solutions for specific problems of object-oriented languages, AOP-related techniques, as well as the combination of AOP and FOP.... ..."

Cited by 1

Table 1 - Mixed Layers groups multiple feature sets together at different levels to create a single layer of the interface

in Layer User Interface

by Bryan Clark, Westford Ma, Jeanna Matthews

"... In PAGE 3: ... If the user has only the need for advanced document editing features but advances on features like printing, then they will have to learn both at the same time, when this may not be their goal. Table1 describes a sample set of features that might be present in a blog editor with a Mixed Layer interface. Each layer may consist of font style features, text alignment features, list features, link features and picture features.... ..."

Table 2. ROC equal error rates of the 4-layer classifier with spatial relation features compared to 1-layer classifier and 4-layer classifier without spatial relation features.

in E.N.: A hierarchical object recognition system based on multiscale principal curvature regions

by Wei Zhang, Hongli Deng, Thomas G. Dietterich, Eric N. Mortensen 2006

"... In PAGE 4: ... In order to test the value of our hierarchical structure, we compared the equal error rates of the whole 4-layer system (denoted as 4-layer with spatial) to the best single layer classifier (1-layer). The results are summarized in the second and third columns of Table2 . In the forth column of Table 2, we show the performance of the 4-layer system without spatial relation (4-layer without spatial) to test the utility of the spatial configuration descriptor.... ..."