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Table 1: AUC score on graphs of size n for six di erent mod- els (A) True (B) Model learned using DSNL, (C) Random Model, (D) Simple Counting Model, (E) Multidimensional Scaling with time, and (F) MDS without time.

in Dynamic Social Network Analysis using Latent Space Models
by Purnamrita Sarkar, Andrew W. Moore
"... In PAGE 6: ... Figure 4 shows the ROC curves for the third timestep on a test set of size 160. Table1 shows the AUC scores of our approach and the ve alternatives for 3 di erent sizes of the dataset over the rst, third, and last time steps. As we can see, from the gures in all the cases, the true model has the highest AUC score, followed by the model learned by DSNL.... ..."

Table 4. YAM2 operations As everything in a multidimensional model, operations are also marked by the duality fact-dimensions . Table 4 shows the operations in two columns. The first one contains those operations having effect on the subject of analysis (i.e. Fact, Cell, and Measure). They select the part of the schema we want to see. In the other column, there are those opera- tions affecting the point of view we will use in the analysis (i.e. Dimension, Level, and Descriptor). They allow to re- organize the data, modify their granularity, and focus on a specific subset, by selecting the instances we want to see.

in YAM2 (Yet Another Multidimensional Model): An extension of UML
by unknown authors

Table 1. A Typology of Multi-Dimensional Evaluation Methods* Type of

in Integrating Multi-criteria Analysis and GIS for Land Condition Assessment: Part II - Allocation of military training areas
by Guillermo A. Mendoza, Alan B. Anderson, George Z. Gertner 2002
"... In PAGE 4: ... There are a limited number of options (training areas) to choose from which are obviously discrete. In this study, two of the six types of models described in Table1... ..."
Cited by 1

Table 2. Mapping among Data Models

in Research issues in data warehousing
by Ming-chuan Wu, Ro P. Buchmann 1997
"... In PAGE 5: ... However, this does not imply the exclusion of other data models, such as the ER model and the relational model. In Table2 , we show the mapping among the constructs provided by the ER model, the multidimensional model and the relational model. For the sake of simplicity attributes are omitted.... ..."
Cited by 18

Table 3: Correlations between the recognizability of faces in transfer conditions when full-face was learned (top), 3/4-face was learned (center), pro le-face was learned (bottom). assumes a homogeneity of the nature of the information in faces, such that this infor- mation is transfered in an equally e cient fashion across viewpoint change. In this case, the transfer problem would be constrained only by the degree of view change. The correlation data we present here indicates that this simple unidimensional model is not adequate to account for human performance at the level of individual faces. Barring this unidimensionality, we applied a multidimensional factor analysis to the model- and human-generated face data with the hope of being able to establish a better linkage between faces across the di erent view conditions. We thought this a reasonable expectation due to the fact that the model supplements the human data

in Stimulus-Specific Effects in Face Recognition Over Changes in Viewpoint
by Alice J. O'toole, Shimon Edelman, Heinrich H. Bülthoff

Table 1. Some example nursing competencies represented in the competency model

in
by Onjira Sitthisak, Lester Gilbert, Hugh C Davis
"... In PAGE 4: ...ig. 3. Multidimensional space of competency model In this paper, we choose competencies from health care because they are amongst the most sophisticated and challenging to implement [13]. Table1 represents some nursing competencies based on the multidimensional space of the COMBA model. For example, C00 (students are able to use and value ethical principles) comprises C10 (students are able to actively apply ethical principles) and C20 (students are able to actively use professional regulation).... ..."

Table 6 reports the degree of multidimensional

in A Recognition-Based Meta-Scheme for Dialogue Acts Annotation
by Claudia Soria, Vito Pirrelli

Table 6: Multidimensional tag correspondences

in A Recognition-Based Meta-Scheme for Dialogue Acts Annotation
by Claudia Soria, Vito Pirrelli

Table 2 Multidimensional competency set

in unknown title
by unknown authors

Table 5: Example relational database table con- taining the same logical information as Table 4. Location Type Time Revenue

in A Grammatical Approach to Understanding Textual Tables using
by Two-dimensional Scfgs, Dekai Wu, Ken Wing, Kuen Lee
"... In PAGE 4: ... Each dimension is described by a set of attributes. For example, Table5 can be semantically inter- preted using the multidimensional data model de- picted in Figure 1. Likewise, the cross-tabular ta- ble in Table 4 can also be semantically interpreted using the same multidimensional data model in Figure 1.... In PAGE 4: ... Likewise, the cross-tabular ta- ble in Table 4 can also be semantically interpreted using the same multidimensional data model in Figure 1. The value of the first three columns in Table5 are the dimension attributes and the rev- enue values are the measures. In contrast, among previous models, Yang (2002) produces a semantically incorrect recogni- tion of a multidimensional table that inappropri- ately presents the attributes in hierarchical struc- ture.... ..."
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