### Table 1: Categorization of feature selection algorithms in a three-dimensional framework

2005

"... In PAGE 9: ...There exists a vast body of available feature selection algorithms. In order to better understand the inner instrument of each algorithm and the commonalities and differences among them, we develop a three-dimensional categorizing framework (shown in Table1 ) based on the previous dis- cussions. We understand that search strategies and evaluation criteria are two dominating factors in designing a feature selection algorithm, so they are chosen as two dimensions in the framework.... In PAGE 9: ... We understand that search strategies and evaluation criteria are two dominating factors in designing a feature selection algorithm, so they are chosen as two dimensions in the framework. In Table1 , under Search Strategies, algorithms are categorized into Complete, Sequential,andRan- dom. Under Evaluation Criteria, algorithms are categorized into Filter, Wrapper,andHybrid.... In PAGE 10: ... Within the Wrapper category, Predictive Accuracy is used for Classification,andCluster Goodness for Clustering. Many feature selection algorithms collected in Table1 can be grouped into distinct categories according to these characteristics. The categorizing framework serves three roles.... In PAGE 10: ...nd Random. Both groups have more than one algorithm available 1. Third, the framework also reveals what are missing in the current collection of feature selection algorithms. As we can see, there are many empty blocks in Table1 , indicating that no feature selection algorithm exists for these combinations which might be suitable for potential future work. In particular, for example, current feature selection algorithms for clustering are only limited to sequential search.... ..."

Cited by 27

### Table 9. The Three-Dimensional Green Scaled Green List with N = 250 N-values for which

1993

"... In PAGE 20: ... Table9 gives a breakdown of the distribution of lattices in this list and their quality. Here L(N) is the lower bound on 3(N) based only on the lattices in this list.... ..."

Cited by 4

### Table 3. Maximum Simulated Likelihood Estimation Results for Three Dimensional Integration

in Abstract Quasi-Random Maximum Simulated Likelihood Estimation of the Mixed Multinomial Logit Model

"... In PAGE 19: ... For the PMC and QMC methods, this is not always the case. For example, the parameter-based error measures increase between 500 and 1000 draws for the PMC method in three dimensions ( Table3 ). Similarly, the parameter-based error measures increase between 75 and 100 draws for the QMC method in three dimensions.... In PAGE 31: ... Maximum Simulated Likelihood Estimation Results for One Dimensional Integration Table 2. Maximum Simulated Likelihood Estimation Results for Two Dimensional Integration Table3 . Maximum Simulated Likelihood Estimation Results for Three Dimensional Integration Table 4.... ..."

### Table 3. Guidelines for positioning atmosphere content in the three-dimensional model space of warmth, activity, and attention Factor Lights Audio Video

"... In PAGE 9: ... A video artist was involved in creating video material for the atmosphere model. The guidelines listed in Table3 were used to select and design the media content for the model, but they should only be considered as rules of thumb, since they are based on the results of the rating sessions for the mood boards. The resulting model space was validated by users.... ..."

### Table 4: Three dimensional table of a132a145a238 a120a5a239 a135 for stripline structures.

1999

"... In PAGE 12: ...2 Effective Coupling Model Based on those studies on micro-stripline and stripline structures, we proposed formula (2) to model the coupling coefficient a8 a55a57a56 between two wires a53 and a54 : a8 a55a58a56a60a59a80a229 a85a231a230a6a232a15a233 (2) where a20 is the pitch spacing between wire a53 and wire a54 , and a234 and a235 are constants depending on the wire width2 and P/G plane distance a191 . A two-dimension table for a234 and a235 (see an example in Table 3) can be built for micro-stripline structure, and a three-dimension table for a234 and a235 (see an example in Table4 ) can be built for stripline structure. We call (2) as a8a10a9a12a11a13a11 model for micro-stripline and stripline structures.... ..."

### Table 4 The 45 combinations of base pairing patterns found in the set of loop 785-797 three-dimensional structures gen- erated by the MC-SYM program.

"... In PAGE 7: ... The conformational search space size of the selected spanning tree is 1023 corresponding to 3375 different combinations of base pairing patterns. MC-SYM generated 33998 consistent three- dimensional structures, composed of 45 different combinations of base pairing patterns (see Table4 ). The RMS deviation among the 45 classes vary from 2.... ..."

### Table 1: The table shows heuristic estimates of the number of iterations and of the computational cost for BEM and for FEM. Based on these observations, we nd it interesting to consider the application of FEM for solving the Laplace equation in the three-dimensional water volume

"... In PAGE 25: ...84% 2.34% Table1 0: The table shows, for the test problem studied in section 6.2, maximum deviation of the computed total energy, obtained on di erent meshes, from the exact value.... ..."

### Table 2. Three-dimensional aVOR gain change prediction performance comparisons between the neural network model (NNM) and the double sinusoid fit 1 and 2 (DS1 and DS2).

"... In PAGE 26: ...results in Table2 . Since the goal of the sinusoidal fits was to minimize the mean square errors, both the single or double sinusoidal models performed better than the neural network model for root mean square errors (Tables 1, 2).... In PAGE 26: ... 4 and 5) was the result of minimizing the eye velocity error at a particular head orientation. When quantitatively correlated to the least-square-error fits of the data in one dimension (Table 1) and in three dimensions using a double sinusoidal fit ( Table2 ), the neural network model predictions of gain changes over all space were ... ..."

### Table 1. Three-dimensional abscissa counts

"... In PAGE 10: ... The fth abscissa count Nprev is readily gleaned from the cited literature. The three-dimensional abscissa counts listed in Table1 are all precisely as de ned above. The four-dimensional abscissa counts listed in Table 2 are also precisely as de ned above for 13.... ..."

Cited by 2

### Table 6. Three-dimensional compressible derivatives

1997

"... In PAGE 5: ... In this test, the cost function is a combination of the lift and drag coefficients so that only one adjoint solution is required. The derivatives of the cost with respect to the angle of attack and the Mach number as well as the derivatives with respect to four of the shape parameterization variables are shown in Table6 . As can be seen, the consistency between the derivatives obtained with the ad- joint formulation and finite differences is excellent.... ..."

Cited by 68