### Table 2. Frequent pattern set comparison.

"... In PAGE 9: ... The generated databases thus fulfil the support differ- ence demands we formulated in Equation 3. The third column of Table2 contains the average supports of item sets that are newly found in the gener- ated databases; these supports are very low. All this together clearly shows that there is a large pattern- similarity, thus showing a high quality according to our problem statement.... ..."

### Table I. Numerical results for the flrst order algorithms on 40 one-dimensional problems.

2003

Cited by 2

### Table II. Numerical results for the second order algorithms on 40 one-dimensional problems.

2003

Cited by 2

### Table 1: Average cost for various one dimensional data sets

1994

"... In PAGE 7: ...Table 1: Average cost for various one dimensional data sets Table1 shows the average costs with and without reduction for various sized data sets using enumeration. Problem sizes greater than 11 are too time consuming to enumerate.... In PAGE 7: ... Problem sizes greater than 11 are too time consuming to enumerate. Table1 clearly shows how chromosome reduction remains e ective as the problem size is increased. Each of the data sets has the same pattern of cost distribution behavior as the original 6 package data set.... ..."

Cited by 1

### Table 3: Training pattern set in 4D

in A Binary-input Supervised Neural Unit that Forms Input Dependent Higher-order Synaptic Correlations

"... In PAGE 5: ... The neural unit generalized the remaining 7 patterns in consistency with the Boolean function f( ~ X). Example 3: The neural unit is trained with the 10 patterns, shown in Table3 , that are generated using the Boolean function f( ~ X) = (x1 x2) ^ (x3 x4) where represents the XOR operation. The neural unit comes up with the relation ( ~ X) = (?0:95 + 0:06x1)(?0:94 + 0:82x2)(0:07 + 0:75x3)(0:04 ? 0:77x4) + (?0:05 ? 0:93x1)(?0:64 ? 0:62x2)(?0:93 + 0:00x3)(0:89 ? 0:01x4) = ?0:49x1 ? 0:48x1x2 ? 0:52x3x4 + 0:45x2x3x4 = 0:5(?x1 ? x1x2 ? x3x4 + x2x3x4) = ?0:5x1(1 + x2) ? 0:5x3x4(1 ? x2) which can be interpreted as: ( ~ X) = ( ?x3x4 if x2 = ?1 ?x1 if x2 = +1 and is represented by the Boolean function: ( ~ X) = ( x1 ^ x2) _ ((x3 x4) ^ x2) which is a simpler relation than the one used in generating the pattern set.... ..."

### Table 11. Parameters used in MOC3D simulation of transport in a one-dimensional, steady-state flow system

"... In PAGE 9: ... Numerical (MOC3D) and analytical solutions at three different locations for solute transport in a one-dimensional, steady flow field. Parameter values for this base case are listed in Table11 .... In PAGE 9: ...0 cm, Dxx = 0.1 cm2/s, and other parameters as defined in Table11 ) .... In PAGE 10: ...01 s-1. All other parameters as defined in Table11 .... In PAGE 90: ...APPENDIX C: ANNOTATED EXAMPLE INPUT DATA SET FOR SAMPLE PROBLEM This example input data set is the one used to generate the solution for the base case in the one-dimensional steady-state flow problem. Parameter values are indicated in Table11 and selected results are shown in fig.... ..."

### TABLE I COMPLEXITY OF ONE-DIMENSIONAL FFT ALGORITHMS

1986

Cited by 13

### Table 2: One-dimensional compression by retention of largest coefficients.

1999

"... In PAGE 20: ... For a fair comparison, we retained the same number of the largest coefficients for each transform, then inverted the cascade algorithm to reconstruct the signal. The results are shown in Table2 and Figure 11.... ..."

Cited by 42

### Table 1 Truth table of best-of-run individual from generation 25 for the one-dimensional cellular automaton problem. West X East Result

1993

"... In PAGE 15: ...996:(AND (OR (OR (NOT (OR E E)) (NOT (OR (OR (AND W W) (OR E (NOT (NOT X)))) (NOT (AND (AND (AND X X) X) (AND (AND W W) (AND W E))))))) (OR (AND W W) (AND E E))) (OR (NOT (OR (NOT (OR (NOT W) (NOT (OR (NOT W) (OR E X))))) (NOT (OR (OR (NOT (NOT X)) (OR E W)) W)))) (NOT (OR E (OR (OR (NOT W) (AND (OR X X) (NOT E))) (AND (OR X X) (AND X E))))))). Table1 shows that this S-expression is rule 30 (00011110 in binary) and is therefore equivalent to Wolfram apos;s cellular automaton randomizer. Table 1 Truth table of best-of-run individual from generation 25 for the one-dimensional cellular automaton problem.... ..."

Cited by 8