### Table III. The translation from the de- cision point in the classical algorithm (from Table 3) to a 10-bit output vector for the network. In the output vector, the first bit indicates whether the presented mush- room is edible (1) or not (0). One of the other bits is turned on to indicate the de- cision point in the classical algorithm at which the edible/not edible decision would have been made

### Table 1: Classical algorithm

### Table 1. results obtained on Intel Xeon 3Ghz with 4Gb of memory : cat.: cate- gory of example (PNT = Petri nets with transfer arcs, PN = (unbounded) Petri net); P: number of places; T: number of transitions; EEC: EEC algorithm; Pre + Inv: Backward approach, using invariant heuristics; Pre: same without invariants. All the memory consumptions in KB and times in second. the channels when a transition is red. This model is well-studied, see e.g. [3, 2]. In particular, the Simple Regular Expressions (sre), a symbolic representation for downward-closed sets of states of LCS, have been de ned. Algorithms to symbolically compute classical operations, such as the union, intersection or the Post, have been devised. In the sequel, we will rely on this background.

2005

"... In PAGE 11: ... We have run the prototype on about 30 examples from the literature. Table1 reports on selected results. The case studies retained here are mainly abstractions of multi-threaded Java programs (most of them taken from [13]).... ..."

Cited by 2

### Table 4: Comparisons on minmax metric among Evolution- ary Algorithms and Classical Methods. Method Best mean( ) Avg. Pop.

"... In PAGE 4: ... The best result and average fitness function value of population, averaged on 30 independent runs, are ob- tained by eGA; while the best mean solution is obtained by DE. The Table4 shows a different task for the parameter ex- traction. In this case the maximum absolute error on the point of data set is minimized in order to determine the best approximation for the circuit model.... ..."

### 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. E ciency of parallel algorithms

1998

"... In PAGE 17: ... The Schwarz alternating procedure with overlapping has been used. The e ciency of parallel iterative algorithms is reported in Table1 using the classical de nition of e ciency: e = t1 tp 1 p,wheretpdenotes the computing time using p processors. Results are given for discretized domains with 25000 points.... In PAGE 17: ... Results are given for discretized domains with 25000 points. From Table1 it can be seen that the e ciency of asynchronous iterations with order intervals is better than the e ciency of parallel synchronous iterations. Idle time due to synchro-... ..."

Cited by 10

### Table 1: The syntax of ClassicJavagc

"... In PAGE 3: ... For those unfamiliar with ClassicJava, its typing and reduction rules are included in the Appendix. The set of programs in Classic- Javagc is de ned by the syntax in Table1 . A program P is a set of class and interface de nitions followed by an expression.... ..."

### 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.... ..."