### Table 2: Parameters to the Probabilistic Counting Al- gorithm

1996

"... In PAGE 8: ... The algorithm based on probabilistic counting esti- mates the size of the cube to within a theoretically pre- dicted bound. The values of the parameters we used are shown in Table2 . The estimate is accurate under widely varying data distributions, ranging from uniform to highly skewed.... ..."

Cited by 66

### Table 2: Parameters to the Probabilistic Counting Al- gorithm

1996

"... In PAGE 8: ... The algorithm based on probabilistic counting esti- mates the size of the cube to within a theoretically pre- dicted bound. The values of the parameters we used are shown in Table2 . The estimate is accurate under widely varying data distributions, ranging from uniform to highly skewed.... ..."

Cited by 66

### Table 4: Demonstrative Resolution Al- gorithm

2000

"... In PAGE 39: ... If this, in turn, is successful, the pronoun is classi ed as DDPro, if it is unsuccessful it is classi ed as VagPro, indicating that the pronoun cannot be resolved using the linguistic context. The procedure is similar in the case of demonstratives ( Table4 ). The only di erence is that (case 3) and (case 4) are reversed to capture the preference for demonstratives to be discourse-deictic (see Section 4).... ..."

Cited by 24

### Table 1: The number of instructions used in the al- gorithm

1993

"... In PAGE 3: ... PROCEDURE Lsb (y) yr:= (((y P) AND B) + C2) AND C1; yr:= yr AND C1; k:= ShiftRight (yr P, (t ? 1) s) AND S; RETURN k END Lsb; Algorithm 1: Computation of the least signi cant set bit in domain of s = dpm + 1e bits Finally we put pieces together and present the complete algorithm (Algorithm 2) for words from a domain of m bits. The number of instructions used in the algorithm (if the call to function Lsb is unfolded) is presented in Table1 . It does not include the as-... ..."

Cited by 13

### Table 1: The Sampling Importance Resampling Al- gorithm

"... In PAGE 3: ... Resampling is used to obtain samples with equal weights in order to facilitate sampling from the mixture in (3). The algorithm is given in Table1 for completeness. The dynamic (motion) model is encapsulated by the tran- sition density p(xt+1jxn t).... ..."

### Table 1: Parameters for the simple molecular al- gorithm.

### Table 3. The Program Supervision Engine al- gorithm

### Table 1. Minimization by the half quadratic al- gorithm

### Table 1. Recognition rates for the different al- gorithms.

"... In PAGE 3: ... We build non-overlapping training and test sets of face images from the beginning and the end of the meeting simulating the planned use of the system. Table1 compares the recognition rates of the grow- ing Gaussian mixture model with two standard algorithms based on 500 training and 200 test images for each of the seven models. The first alternative procedure implements the conventional eigenface approach where the eigenspace representations of all training images are averaged to a sin- gle model vector.... ..."

### Table 3: Example of applying the bidirectional al- gorithm.

"... In PAGE 3: ... A better approach is to apply the method in both directions simulta- neously choosing to add gates at the input side or the output side. To see how this works, consider the initial reversible spec- i cation in Table3 , column (i). The basic algorithm would require that we invert each of a0, b0 and c0 to make f+(0) = 0.... ..."