### Table V: Linear, tree-based and optimized network sizes.

1995

Cited by 6

### Table 1 Experimental results (aggregated for linear and tree recursions)

1998

Cited by 6

### Table 1 Experimental results (aggregated for linear and tree recursions)

1998

Cited by 6

### Table 2: Comparing the explanatory power of regression trees and linear models.

"... In PAGE 5: ... We also constructed regression tree and linear models for subsets of the data cor- responding to the two different systems, Annie and Elvis. As shown in Table2 , the explanatory power of the tree models was quite similar to that obtained via linear models, and similar sets of predictor variables were observed. However, there is reason to be cautious with respect to the linear models.... ..."

Cited by 2

### Table V: Linear, tree-based and optimized network sizes. Topology #States #Labeled arcs #Empty arcs Total arcs

59

### Table 5.13: Comparison of the performance and accuracy of a strictly linear SVM-tree and one using the non-linear extension.

2006

Cited by 1

### Table 3: Training iteration run time (seconds) for LinearCRF and TreeCRF

2004

Cited by 19

### Table 1. Summary of results of error-rate in classification problems of univariate tree and Linear-Bayes. The reference algorithm is the univariate tree.

"... In PAGE 13: ...6 The last line shows the p values associated with this test for the results on all datasets with respect to the reference algorithm. Table1 presents a comparative summary of the results of simple algorithms: all the univariate trees and LinearBayes. In this case the reference algorithm is FT-Univ.... ..."