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Table 1. The Calculated Results for Analyzed Data-Set

in Polynomial Neural Network for Linear and Non-linear Model Selection In Quantitative-Structure Activity . . .
by Igor V. Tetko, Tetyana I. Aksenova, Vladimir V. Volkovich, Tamara N. Kasheva, Dmitry V. Filipov, William J. Welsh, David J. Livingstone, Alessandro E. P. Villa 2000
"... In PAGE 9: ... In order to have easy interpretable models, we have fixed the maximal number of terms in the equation to be equal to 8 and the maximum degree of polynoms to be equal to 3. The calculations performed using the select params option of the ANALYSIS are summarized in Table1 . The number of stored models was 3.... In PAGE 9: ... It was shown that the use of significant variables, as detected by MUSEUM, = improved PLS results (compare data in column 7 vs. column 6 in Table1 ). The similar tendency was also observed if only variables found to be relevant by the PNN algorithm were used in the cross-validation calculations (compare the last and 7 columns of Table 1).... In PAGE 12: ... b Number of significant PLS components. c The cross-validated q2 calculated using input variables optimized by MUSEUM approach (unless not stated otherwise the PLS results are from Table1 and 15 of (2)). d Number of input variables selected by PNN.... ..."
Cited by 2

Table 1: Data on the Interval Tree structure for the two analyzed datasets (times are CPU seconds).

in Optimal Isosurface Extraction from Irregular Volume Data
by P. Cignoni, C. Montani, E. Puppo, R. Scopigno
"... In PAGE 5: ... Numerical results have been obtained on an SGI Indigo (100MHz R4000 cpu, 8K instruction and 8K data caches, 1MB secondary cache, 64MB RAM). Table1 reports data on the complexity of both the datasets used and the associated interval trees: the resolution of the datasets (n, the number of sites); the number m of intervals, which is equal to the numberof tetrahedral cells; the depth andthe numberh of nodes of the interval tree; and the time (in seconds) required to build this data structure. The space complexity can be simply calculated from these data us- ing the space complexity defined in Section 4, 4h + 4m memory words.... ..."

Table 4.5: Performance of general promoter prediction programs averaged over 10 analyzed datasets. Values of Se, ppv and cc are shown with their standard deviations (stdev).

in ACKNOWLEDGEMENTS
by Rajesh Chowdhary 2006

TABLE I ACCURACY FOR THE ANALYZED METHODS ON BENCHMARK DATASETS TOGETHER WITH THE P-VALUE OF THE WILCOXON RANK TEST

in An ensemble of Weighted Support Vector Machines for Ordinal Regression
by unknown authors

TABLE II MEAN ABSOLUTE ERROR OF THE ANALYZED METHODS ON BENCHMARK DATASETS TOGETHER WITH THE P-VALUE OF THE WILCOXON RANK TEST.

in An ensemble of Weighted Support Vector Machines for Ordinal Regression
by unknown authors

TABLE III Comparison of peptide and protein identifications from a plasma proteome profiling dataset analyzed using different criteria (59)

in Advances and challenges in liquid chromatography-mass spectrometry-based proteomics profiling for clinical applications
by Wei-jun Qian, Jon M. Jacobs, Tao Liu, David G. Camp Ii, Richard D. Smith 2006
Cited by 1

Table 4.2: Performance of 10 histone promoter structure Bayesian models (with different DAG structures) averaged over 10 analyzed datasets. Values of Se, ppv and cc are shown with their standard deviations (stdev). Also shown is the performance of automatically generated models using MCMC and Greedy search.

in ACKNOWLEDGEMENTS
by Rajesh Chowdhary 2006

Table 4 shows the results of running the seven categorical methods over the three arti cial datasets. In the following paragraphs we analyze these results, and compare the methods based on the datasets used.

in Feature Selection for Classification
by unknown authors
"... In PAGE 20: ... Table4 : Table showing the features selected for the three datasets. RA { Relevant Attributes I-T { Inconsistency Threshold This is one of many found.... ..."

Table 1. Benchmarks from SPECint2000 se- lected, input datasets used, number of in- structions analyzed and number of unique loops captured.

in Runtime Predictability of Loops
by Marcos R. de Alba, David R. Kaeli
"... In PAGE 3: ... We capture statistics, avoiding pro- gram initialization (the first 100M-250M instructions), and capture a sample of 500M-1B instructions, depending on the benchmark being studied. 5 Results Table1 shows the benchmarks and input files used, the instructions executed and the number of unique dynamic (i.e.... In PAGE 3: ... In most cases, programs containing loops that iterate a large number of times, also contain fewer loops. A good example of this case is in gcc, where the number of dy- namic loops is large (as we can see in Table1 ), but each loop executes a much smaller number of times (as we can see in Table 2). The opposite case can be observed for gzip.... ..."

Table 1. Benchmarks from SPECint2000 se- lected, input datasets used, number of in- structions analyzed and number of unique loops captured.

in Runtime Predictability of Loops
by Marcos De Alba
"... In PAGE 3: ... We capture statistics, avoiding pro- gram initialization (the first 100M-250M instructions), and capture a sample of 500M-1B instructions, depending on the benchmark being studied. 5 Results Table1 shows the benchmarks and input files used, the instructions executed and the number of unique dynamic (i.e.... In PAGE 3: ... In most cases, programs containing loops that iterate a large number of times, also contain fewer loops. A good example of this case is in gcc, where the number of dy- namic loops is large (as we can see in Table1 ), but each loop executes a much smaller number of times (as we can see in Table 2). The opposite case can be observed for gzip.... ..."
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