### Table 1: Details of the dataset. Number of pages taken from individual categories are not shown due to the enormous size of the latex table. But interested reader can contact authors to get the details.

2005

Cited by 8

### TABLE I DETAILS OF THE DATASET. THE NUMBER OF PAGES TAKEN FROM INDIVIDUAL CATEGORIES ARE NOT SHOWN DUE TO THE ENORMOUS SIZE OF THE LATEX TABLE, BUT THE INTERESTED READER CAN CONTACT AUTHORS TO GET THE DETAILS.

Cited by 4

### Table 1 shows the values of the geometric and second-order powers for several useful distributions6. Except from the uniform and the Gaussian, all the other distributions in the table are algebraic- tailed, parameterized by the tail constant and a scale parameter . Note that for values of 6The interested reader is referred to [9] for the derivations.

1997

"... In PAGE 12: ... at = ?(1+ 2 ) p ?( 2 ) : ac = p?(1+ p ) 2?(1 p)?( p ) : (z) = d dz log ?(z) = ?0(z) ?(z) is the psi function. Table1 : Geometric and second-order powers of several useful distributions. While the second-order power becomes in nite (and consequently useless) for values of lt; 2, the geometric power is always well de ned, no matter the level of impulsiveness of the distribution.... ..."

Cited by 1

### Table 1: Execution of the RPNI algorithm. If jjS+jj and jjS?jj denote the sums of lengths of each example in S+ and S? re- spectively then it can be shown that the time complexity of the RPNI algorithm is O((jjS+jj + jjS?jj) jjS+jj2). The interested reader is referred to [Oncina amp; Garc a, 1992] for the correctness proof and the analysis of the time complexity of the RPNI algorithm.

1997

"... In PAGE 10: ... M^ accepts the negative example 2 S? and hence the partition remains unchanged. Table1 lists the di erent partitions ~ obtained by fusing the blocks of 0, the par- titions ^ obtained by deterministic merge of ~ , and the negative example (belonging... ..."

Cited by 21

### Tables. Other measurements considered recent enough or important enough to mention, but which for one reason or another are not used to get the best values, appear separately just beneath the data we do use for the Summary Tables. The Particle Listings also give information on uncon rmed particles and on particle searches, as well as short \reviews quot; on subjects of particular interest or controversy. The Particle Listings were once an archive of all published data on particle properties. This is no longer possible because of the large quantity of data. We refer interested readers to earlier editions for data now considered to be obsolete. We organize the particles into six categories: Gauge and Higgs bosons

### Table II shows three models, two of which perform adequately and one which appears to have no predictive power. The result- ing ranks created by the min, avg, and min +avg/2 functions are also shown. Realize that the resulting columns do not equate to f(R1(xi),R2(xi),R3(xi)) = f(oi1,oi2,oi3). The aggregation columns represent R(f(oi1,oi2,oi3)). Particularly for the min function, ties must be solved which is done simply at random. Immediately following any aggregation, decision values are immediately mapped to ranks, or oij. Table II is consistent with Algorithm 1; if desired, an interested reader could recreate the last three columns to reinforce the concept.

2006

Cited by 2

### Table II shows three models, two of which perform adequately and one which appears to have no predictive power. The result- ing ranks created by the min, avg, and min +avg/2 functions are also shown. Realize that the resulting columns do not equate to f(R1(xi),R2(xi),R3(xi)) = f(oi1,oi2,oi3). The aggregation columns represent R(f(oi1,oi2,oi3)). Particularly for the min function, ties must be solved which is done simply at random. Immediately following any aggregation, decision values are immediately mapped to ranks, or oij. Table II is consistent with Algorithm 1; if desired, an interested reader could recreate the last three columns to reinforce the concept.

2006

Cited by 2

### Table 1: A constrained SLDE{refutation of the running example. The underlined termes are the selected resources in need, constraints are simpli ed, and resolution steps upon action subgoals are omitted. In step 1, 3, and 6 the actions a1 , a2 , and a3 are chosen, respectively. In step 4 the actions a1 and a2 are still not related, but after consuming hcl (b); a1i in order to produce hcl (b); a2i the constraint a1 lt; a2 is added in step 5. One should observe that in step 6 a resource hcl (c); aWi is available. The interested reader is encouraged to check that selecting this resource for producing hcl (c); a1i eventually leads to an inconsistent constraint.

1996

"... In PAGE 7: ... The computation is by SLDE{resolution [11] extended by a consistency check of the accumulated constraints. Table1 shows such a refutation for the running example. 9 One should observe that Y < Z implies Y 6 = Z .... ..."

Cited by 1