### Table 3: Yeast protein data zero-one loss results.

2004

"... In PAGE 4: ...004. We obtained similar results with experiments on the yeast protein data, shown in Table3 . CI resulted in the lowest zero- one loss and its loss was significantly different than the zero- one loss of all other models.... ..."

Cited by 43

### Table I. The weights for each edge of G are computed via TagLink string metric, [Camacho and Salhi 2006]. In each case, the quality is computed for 31 range values of parameter k, between zero and one. The results are displayed in Fig. 1.

### Table 2: Results for general zero-one problems.

1996

"... In PAGE 17: ... This was one of the early computational breakthroughs in combinatorial optimization, as most of the problems were considered not amenable to exact solution within reasonable time. Table2 gives a summary of the computational results. The valid inequalities were generated and added in the root node of the branch-and-bound tree only.... In PAGE 17: ... For more details about preprocessing we refer to Part II of this article. In Table2 vars, constr., and ineq.... ..."

Cited by 4

### Table 9: Results for general zero-one problems.

1996

Cited by 4

### TABLE 1 NUMBER REPRESENTATIONS FOR SEQUENCES OF ZEROES AND ONES

### Table 1: The linear spans of the zero-one sequences from monomial hyperovals.

"... In PAGE 6: ...omputer results when m is small (with the help of R. M. Wilson). The method we used in the calculation is to nd a trace representation for the sequence in question. We list the results in Table1 below. Note that here the sequences are 0,1 sequences which are de ned as follows.... In PAGE 7: ...Table1 lists the sizes of the eld. The next three columns list the linear spans of the 0,1 sequences from the Segre hyperoval, and the two Glynn hyperovals respectively.... In PAGE 7: ...The next three columns list the linear spans of the 0,1 sequences from the Segre hyperoval, and the two Glynn hyperovals respectively. The linear spans in the rst column of Table1 are particularly interesting. The second factors of the linear spans in the rst column satisfy a recursive relation like that of Fibonacci numbers.... ..."

Cited by 1

### Table 1: The linear spans of the zero-one sequences from monomial hyperovals.

"... In PAGE 6: ...omputer results when m is small (with the help of R. M. Wilson). The method we used in the calculation is to find a trace representation for the sequence in question. We list the results in Table1 below. Note that here the sequences are 0,1 sequences which are defined as follows.... In PAGE 7: ...Table1 lists the sizes of the field. The next three columns list the linear spans of the 0,1 sequences from the Segre hyperoval, and the two Glynn hyperovals respectively.... In PAGE 7: ...The next three columns list the linear spans of the 0,1 sequences from the Segre hyperoval, and the two Glynn hyperovals respectively. The linear spans in the first column of Table1 are particularly interesting. The second factors of the linear spans in the first column satisfy a recursive relation like that of Fibonacci numbers.... ..."

Cited by 1

### Table II Correlations between Insurance Premiums Estimated from Different

### Table 2: Cross-entropy and zero-one error statistics on test data based on 25 runs. The test errors were obtained by 2-fold cross-validation.

2005

"... In PAGE 12: ... We purposely do not give the training errors for all methods, since a data analyst is primarily interested in out-of-sample performance of the models and a low training error gives no guarantee for a low test error. Table2 gives statistics on the cross-entropy and zero-one prediction errors for the ketchup and peanut butter datasets based on 25 runs. In each run, a bootstrap replicate of the dataset was created to build to model, as described in Section 4.... ..."

### Table XVIII. Optimization scenarios and models. Scenario Source Destination Channel Optimization Model 1 Single Single Single Zero-One KP 2 Single Single Many MKP

2005