### Table 1: Classical algorithm

### Table 1: PLM description of classical algorithms

"... In PAGE 12: ... A taxonomy Using our short codes allows a quick description of the pre- sented examples enlightening relations and differences. Table1 gives the PLM description of different algorithms. This three-dimensional space of search algorithms presents many untried spots.... ..."

### Table 4. Classical Montgomery Multiplication Algorithm

2001

### Table 1. Comparison between the classical estimation for RBF and the MRBF algorithm.

"... In PAGE 21: ... From these figures it is evident that MRBF approximates better the boundaries between classes than classical statistical estimators for the RBF. The improvement is clear for MRBF in all cases considered in Table1 . However, when the mixture of bivariate normal distributions is contaminated with uniform noise (e.... In PAGE 21: ... By using the Mahalanobis distance (8) instead of the Euclidean one, we obtain better results for both algorithms, except in the case when we use classical es- timators for the uniform contaminated model, Equations (43) and (44). We can see from Table1 that the MRBF algorithm with the Mahalanobis distance gives the best results. In Figure 6, we evaluate the global convergence of the algorithms in the case of distribution I.... ..."

### Table 1. Results for the method of squares, DSG and classical algorithms

in Edited by

2005

"... In PAGE 65: ... Figure 11: Several examples of the complete process of tracking and posture recognition with one person Figure 12: An example of the complete process of tracking and posture recognition with several people in severe lighting conditions The system yields to an average mean recognition rate of 95% which makes it useful in several real-world applications. Table1 shows the recognition rate computed for each posture. Table 1: Successful recognition rates for each posture Posture type Success rate Standing 98% Pointing left 95% Pointing right 94.... In PAGE 65: ... Table 1 shows the recognition rate computed for each posture. Table1 : Successful recognition rates for each posture Posture type Success rate Standing 98% Pointing left 95% Pointing right 94.5% Stop (pointing both left/right) 96% Left arm raised 92% Right arm raised 93% Both arms raised 97% AVERAGE: 95.... In PAGE 70: ... Figure 8 shows the minutiae selected by the supervisor, by the method of squares, by the classical methods and by the DSG method respectively. Table1 shows the indexes of accuracy based on the sample image plotted in Figure 7. Supervisor Squares DGS Classical Figure 7: Example of identified minutiae for a sample image with the method of squares, DSG and classical algorithms.... In PAGE 79: ... The environmental change, as already mentioned, represented a relevant strat- egy across scenarios (Tab 1). Table1 . Scenarios by strategies The influence of socio-demographic variables on coping strategies showed a highly variable pattern.... In PAGE 93: ...B-01 B-01 R-01 R-02 B-03 B-04 D-02 D-05 B-05 B-06 D-07 TRAYRACK KITCHEN D-03 D-06 D-04 B-07 B-08 B-09 HALL R-03 R-04 R1 R2 R3 R4 R5 Figure 2: The environment Env-A used for our tests. Env-A Env-B # Rooms 6 10 # Doors 7 14 # Beds 9 18 # Robots 5 8 # Status Multi-valued Variables 115 312 Table1 : The main characteristics of the two considered en- vironments. It is worth noticing that the cases have been deliberately created to be very challenging for both the monitoring and diagnosis tasks.... ..."

### Table 2: Applications of the algorithm involving classical O.P.

"... In PAGE 8: ...Proceedings of the IWOP apos;96 In case = , it reduces to a two-terms recurrence relation, whose solution is [31, 21]: Cm(n) = n m ! 1 ? n?m ( ? )n?m : (19) In case = , the (new) result is [3] Cm(n) = n m ! ?(n + ) ?(m + ) ? (1 ? )(1 ? ) n?m : (20) Table2 presents informations on explicit results or on the structure of the recurrence relation satis ed by Cm(n) connecting essentially Classical Orthogonal Polynomials. Many results are already published, in print or in preparation.... ..."

### TABLE 1. Classical mathematical constructs reborn as important con- temporary algorithms

1999

Cited by 3

### Table 1. Theoretical comparison between the classical addition chain algorithms.

1997

Cited by 3