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Table 2: As the number of salient objects decreases and the number of distractors increases, VISOR apos;s con dence about the scene decreases gradually.

in Analyzing Scenes in a Neural Network Model of Schema-Based Vision
by Wee Kheng Leow, Risto Miikkulainen
"... In PAGE 9: ...ith varying number of salient objects as well as distractor objects, i.e., those that appeared only in other scenes. Table2 (Experiment 4) shows that as the number of salient objects decreases and the number of distractors increases, VISOR found the input scene more and more confusing, and its con dence about the scene was lowered gradually. The activity of the workbench schema was a ected by the competition from other scene schemas.... In PAGE 10: ... As in the previous experiment, VISOR was also presented test scenes that vary in the number of salient objects and distractors for recognition. This time, its con dence about the scenes decreased less ( Table2 , Experiment 5) compared to the previous experiment (Table 2, Experiment 4) because the missing salient objects appeared only 50% of the time during training. Therefore, VISOR uses the encoded probabilities to judge how important an object identi es a scene, which further adds to its exibility in representing \soft quot; schemas.... ..."

Table 4 Distractor effect in the patient group

in 1 This work was carried out under the supervision of
by Anna Sorkin, Prof Daphna Weinshall 2006
"... In PAGE 46: ...40 exhibiting the distractor effect and its magnitude are summarized in Table4 . Half of the patients manifested the distractor effect for an auditory rule.... ..."

TABLE X: Distractor Experiment (1 of 1)

in Engineered
by Visibility Warning Signals, Contract Its-l, Theodore E. Cohn, Ph. D

Table 1. Synchronization index (SI) for targets and distractors

in Anticipatory control of long-range phase synchronization
by Joachim Gross, Frank Schmitz, Irmtraud Schnitzler, Klaus Kessler, Kimron Shapiro, Bernhard Hommel, Alfons Schnitzler
"... In PAGE 3: ...e. synchronization and desynchronization increased equally with target positions ( Table1 ). Two means with non-overlapping lines in Fig.... ..."

Table 2: Classification of Systems according to identified important criteria

in unknown title
by unknown authors
"... In PAGE 2: ... References 19 8. List of abbreviations (Not Included in this version) Table 1: Summary of systems, services, Applications and Tools 8 Table2 : Classification of Systems according to Important Criteria 17 Annexure 1:Framework for the Collaborative Program 20 Annexure 2: Minutes of Bonn Meeting 25 Annexure 3a-g: Filled Questionnaires 67 Annexure 4: Detailed Description of all studied systems 83 Appendix 1: Data Flow in NARIMS 102 Appendix 2: Data Dictionary of NARIMS 118 ... ..."

Table 1: The classification of the most important notions of test selection

in A Formal Approach to Practical Test Selection
by Tibor Csöndes, Balazs Kotnyek
"... In PAGE 7: ... We discuss this method in the next section. As a summary, we classified the most important notions of test selection with respect to their properties ( Table1 ). Test cases and test purposes are available in standardized test documents, while subpurposes and requirements are usually not.... ..."

Table 8.3 Importance ranking of the variables in the Bayesian classification model

in unknown title
by unknown authors 2000
Cited by 1

Table 1: Classification of the level of importance of safety culture factors

in EUROCONTROL Experimental Centre
by Eurocontrol Experimental Centre, Jamie Henderson, Rachael Gordon Eec, Rachael Gordon, Centre De Bois Des Bordes 2005

Table 1: The tasks and distractors for conjunctive searches in the interplay trial (Section 4).

in Useful Properties of Semantic Depth of Field for Better F+C Visualization
by Robert Kosara, Silvia Miksch, Helwig Hauser, Johann Schrammel, Verena Giller, Manfred Tscheligi 2002
"... In PAGE 3: ...g., the red and sharp object), while the distractors could have any other combination of the two ( Table1... ..."
Cited by 6

Table 2. The five most important features (listed from most to least important) for classification as chosen by backward selection. (For details on the features, see [18].)

in Hierarchical hardness models for SAT
by Lin Xu, Holger H. Hoos, Kevin Leyton-brown 2007
Cited by 2
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