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Table 4.1: C-LDA vs. LDA Methods m.(1000) auto.+m.(38) auto.+m.(18) auto.+m.(5) auto.

in PATTERN RECOGNITION USING COMPOSITE FEATURES
by Chunghoon Kim 2007

TABLE III. Lattice and internal parameters of our TB results at the equilibrium volume of Ga phase compared with experimental and LDA results. The experimental data taken from the handbook by Wycko [13] were measured at 4.2 K and atmospheric pressure. LDA data are from Ref. [14]. The bulk modulus were also computed and compared with the other results. a (au) b/a c/a Bo (KBar)

in Application of a Tight-Binding Total Energy Method for Al, Ga and In
by Sang H. Yang, Michael J. Mehl, D. A. Papaconstantopoulos 1996
Cited by 2

Table 5: Comparison of the test set performance of the COSSO SVM with those of SVM, LS-SVM, LDA, QDA, Logit, C4.5, oneR, IB, Naive Bayes, and the Majority Rule. The results of the other algorithms are taken from the paper Gestel et. al. (2004). BUPA Ionosphere Pima Indian Sonar MR Wisc. BC

in Variable Selection for SVM via Smoothing Spline ANOVA
by Hao Helen Zhang
"... In PAGE 14: ...Hao Helen Zhang the Wisconsin Breast Cancer data. The basic features of the datasets and the perfor- mances of di erent algorithms are summarized in Table5 . Following Gestel, Suykens, Baesens, Viaene, Vantheienen, Dedene, Moor, and Vandewalle (2004), for each dataset we randomly select 2/3 of the data for training and tuning, and test on the remaining 1/3 of the data.... In PAGE 14: ... We do this randomization 10 times and report the average test set performance and sample standard deviation for the COSSO SVM. The best average test set performances are denoted in bold face for each dataset in Table5 . The ad- ditive COSSO SVM is tted and its performances on these benchmark datasets are very competitive to other algorithms.... ..."

Table 3. Overview of detection reliabilities of the coefficient types attack. The best result was achieved with the SVM using the C-classification with a polynomial kernel (second degree). For mtype 2900 images are used to specify the detection reliability. The training set for LDA and SVM were 2300 images and another 630 images were classified

in Weaknesses of MB2
by Christian Ullerich, Andreas Westfeld
"... In PAGE 11: ... The detection reliability can be increased as depicted in Fig. 4 and Table3 : LDA(T18). (The superscript index states the number of features.... In PAGE 12: ...ig. 4. ROC curves for coefficient types attack on MB1 (left) and MB2 images (right) with 0.02 bpc embedding rate is labelled as LDA(T18) in Table3 . But this method of detection includes the use of the algorithms MB1 as well as MB2 to generate further images, which are needed for the feature set of the classification.... In PAGE 12: ...evel of 50 % is 5.5 % for MB1 and 1.2 % for MB2. Table3 has shown that an SVM with the right parameters can moderately increase the detection reliability. However, these parameters have to be determined for each feature set again.... ..."

Table 2. Results with k latent classes for 18400 dpa docu- ments from Jan 2000. The experiments are named accord- ing to their conflguration including documents (d), sen- tences (s), and use of unigrams (u) and bigrams (b).

in Investigating Word Correlation at Different Scopes - a Latent-Concept Approach
by Gregor Heinrich, Jörg Kindermann, Codrina Lauth, Gerhard Paaß, Javier Sanchez Monzon
"... In PAGE 6: ...C, Trainer, Team, Spiel,...). Table2 contains experimental results for PLSA and LDA on dpa documents for one month, January 2000. This relatively constrained subcorpus (18.... ..."

Table 2 Classification results and rejection thresholds obtained for the Pima Indians Dia-

in by the European Community fund FEDER.
by Carla M. Santos-pereira A, Ana M. Pires B, Centre For
"... In PAGE 16: ...lassifiers. (a) LDA (b) LD (c) MLP. positive case, RP . Table2 summarises the results. Note that for the first four cost combinations (cases a, b, c and d) the rejection option is not activated.... ..."

Tables 5{2 through 5{5 present additional results for the C2H4, F2, H2O, and NH3 molecular systems. One additional trend is that the Vn potential performs better for 80% and 60% reductions of the virtual space, but the Vn 1 potential performs slightly better for the 40% reduction of the virtual space. Because total energies are seldom important, it is more constructive to examine computed energy di erences. In Table 5{6, activation energies for the unimolecular dissociation of the pentazole anion, N 5 , are presented. Again the SCF, LDA/VWN, and PW91 results are quite similar. The results for the FNOs are remarkably good. For the CCSD(T) method, FNOs with only 40% of the virtual space predict an

in High Energy-Density Materials: The Role Of Predictive Theory
by Kenneth John Wilson, Professors N. Yngve Öhrn, Hendrik J. Monkhorst, Er Angerhofer, Jeffrey L, Mr. Anthony Yau, Dr. Erik Deumens, Mrs Coralu Clements 2002

Table 1: Comparison of correct classification rates, after the projection of data onto Sorted PCA + LDA subspace, depending on the classifier: C1 Mahalanobis distance, C2 Euclidean distance, C3 Bayes rule and C4 fuzzy integral method.

in Facial expression recognition by combination of classifiers
by Séverine Dubuisson, Franck Davoine
"... In PAGE 4: ... 4 RESULTS We have tested our method with 373 new samples from the CMU-Pittsburgh database [6], which do not belong to the learning set. We first project the samples into the Sorted PCA + LDA subspace, before recognition of the facial expressions using single classifiers or their combination: the results are reported in Table1 . The tested classifiers are: Mahalanobis distance (C1), Eu- clidean distance (C2), Bayes rules (C3) and their com- bination (C4).... ..."

Table 8. Results of Normalizing Data According to Estimates Made in Subtask 0

in Use of Virtual Environments for Acquiring Configurational Knowledge about Specific Real-World Spaces:
by Thomas E. Von Wiegand, Rebecca Lee Garnett, Nathaniel I. Durlach, Real-world Spaces, Preliminary Experime Nt 1999
Cited by 5

Table 8. Results of Normalizing Data According to Estimates Made in Subtask 0

in Use of Virtual Environments for Acquiring Configurational Knowledge About Specific Real-World Spaces: Preliminary Experiment. PRESENCE: Teleoperators and Virtual Environments 8(6
by Thomas E. Von Wiegand, Rebecca Lee Garnett, Nathaniel I. Durlach, Real-world Spaces, I. Preliminary Experiment 1999
Cited by 5
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