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Table 6: running times of the parameterization algorithms

in Mesh Parameterization for Texture Mapping
by Geoffrey White 2004

Table 3. Example Extensible Operating Systems. The Exokernel and Solaris systems support multiple technologies for extensibility; each technology is shown independently. Most extensibility in Exokernel is achieved by modifying user-level libraries (the first entry), but Exokernel also provides the ability to download code into the kernel (the second entry). Solaris supports build time parameterization in the form of the addition of new file systems or scheduling classes (the first entry). At run-time, the super-user can dynamically assign processes to different scheduling classes (the second entry) and can dynamically download device drivers (the third entry).

in Issues in Extensible Operating Systems
by Margo I. Seltzer, Yasuhiro Endo, Christopher Small, Keith A. Smith 1997
Cited by 12

Table 3. Example Extensible Operating Systems. The Exokernel and Solaris systems support multiple technologies for extensibility; each technology is shown independently. Most extensibility in Exokernel is achieved by modifying user-level libraries (the first entry), but Exokernel also provides the ability to download code into the kernel (the second entry). Solaris supports build time parameterization in the form of the addition of new file systems or scheduling classes (the first entry). At run-time, the super-user can dynamically assign processes to different scheduling classes (the second entry) and can dynamically download device drivers (the third entry).

in 1. Abstract Issues in Extensible Operating Systems
by Margo I. Seltzer, Yasuhiro Endo, Christopher Small, Keith A. Smith

Table 2: Statistics and timings.

in Least Squares Conformal Maps for Automatic Texture Atlas Generation
by Bruno Lévy, Sylvain Petitjean, Nicolas Ray, Jérôme Maillot
"... In PAGE 8: ... Some examples of textured models are shown in Figure 10. Table2 shows the sizes of the data sets, the number of created charts, and the following statistics, obtained on a 1.3 GHz Pentium III (note that the timings for the packing algorithm are not included, since they are negligible): time to segment the model into charts; time to parameterize the charts.... ..."

Table 5.1.1 Parameters tested KNN F. KNN

in CLASSIFICATION OF RUST GRADES ON STEEL SURFACES
by Cem Ünsalan, Aytül Erçil 1998

Table1.Percentage of error rates Table2. Features and the parameters used Bayes Parzen KNN F. KNN Bayes Parzen KNN F. KNN

in Defect Inspection of Wood Surfaces
by Cem Ünsalan, Aytül Erçil 1997
"... In PAGE 20: ...ombination of the results of all the feature extraction algorithms. This is called the hybrid features. Since the cost of false alarm and missed defects are different, the total error can be taken as weighted combination of these two errors. Table1 and table 3 shows these errors in two different rows. In these tables the first row in a cell corresponds to error rate of missed defects.... ..."
Cited by 1

TABLE III Error rates with KNN

in SVMs for Histogram-Based Image Classification
by Olivier Chapelle, Patrick Haffner, Vladimir Vapnik

Table 3 The boosted methods against the weak leaner KNN for force X Compare with KNN Adaboost-M(KNN) A-boosting-M(KNN) Adaboost.M1(KNN)

in Acknowledgements
by Fengming Tang 2006
"... In PAGE 28: ...1.1 The Comparisons of Boosted Methods against the Corresponded Weak Learners Table3 gives the comparisons of Adaboost-M(KNN), A-boosting-M(KNN), Adaboost.M1(KNN) against the weak leaner KNN.... ..."

Table III. Parameter tuning in WORD, kNN and LLSF on Reuters version 3, validation test set Fixed Parameters Tested Values Best Choice(s) Best 11-ptAvgp Best microavgF1

in An Evaluation of Statistical Approaches to Text Categorization
by Yiming Yang 1999
Cited by 347

Table 4: k-NN Query.

in Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces
by Kaushik Chakrabarti, Sharad Mehrotra 2000
"... In PAGE 8: ....2.3 k Nearest Neighbor Queries A k-NN query retrieves a set of objects such that for any two objects , . The algorithm for k-NN queries is shown in Table4 . Like the ba- sic k-NN algorithm, the algorithm uses a priority queue to navigate the nodes/objects in the database in increasing order of their distances from .... ..."
Cited by 91
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