### Table 3. A Decomposition-Tree Learning Algorithm

### Table 2. Optimal decompositions on Alpha 21264

2001

"... In PAGE 4: ... On other platforms such as AMD Athlon and SUN UltraSPARC IIs, benchmark results using our WHT package are available at our SPIRAL (Sig- nal Processing Algorithms ImplementationResearch for Adap- tive Library) [7] web site. Table2 shows the optimal trees chosen by pruning the search space in both approaches. In Table 2, [n] is a straight line unrolled code for 2 n -point WHT.... In PAGE 4: ... Table 2 shows the optimal trees chosen by pruning the search space in both approaches. In Table2 , [n] is a straight line unrolled code for 2 n -point WHT. [[n1], [n2]] repre- sents a tree computationusingCMUapproach and d[[n1],[n2]] , a tree computationusingourapproach.... ..."

Cited by 7

### Table 1. Wavelet Packet Decomposition Tree

1997

"... In PAGE 4: ... Our tree structure is closer to the critical band structure than the decomposition tree used in [6]. Table1 shows the relationship between the designed WP decomposition tree and the real critical bands. This tree separates the wideband audio signal into 38 subbands.... ..."

Cited by 1

### Table 1. Static decomposition algorithm

### Table 1 presents an analysis of the sequences of trees produced via this detransforma-

1998

Cited by 125

### Table 3: Reduction of computational effort of the proposed approach over the standard multiscale algorithm (measured via raw FLOP count). As expected, the benefit increases for more difficult problems: the reduction factor increases for larger trees and more finely sampled domains. The results are based on the tree-like prior of Figure 9.

"... In PAGE 14: ... Our research goal is the development of statistical methods for very large problems, so in this section we focus on computational issues. Table3 shows the improvement in speed of our proposed approach over the standard singly-rooted mul- tiscale algorithm[7, 22], when applied to the Markov random field problem of Figure 9. The extra states in- troduced by our multiply-rooted approach cannot be justified for extremely small or poorly-sampled problems (upper left of Table 3), however as the problem size and sampling density increase (lower right) the decompo- sition offered by the multiply-rooted approach becomes more competitive.... In PAGE 14: ... Table 3 shows the improvement in speed of our proposed approach over the standard singly-rooted mul- tiscale algorithm[7, 22], when applied to the Markov random field problem of Figure 9. The extra states in- troduced by our multiply-rooted approach cannot be justified for extremely small or poorly-sampled problems (upper left of Table3 ), however as the problem size and sampling density increase (lower right) the decompo- sition offered by the multiply-rooted approach becomes more competitive. For large, densely sampled trees, computational improvements in excess of a factor of twenty were observed.... ..."

### TABLE 1: CONVERGENCE PERFORMANCE AND APPROXIMATION ERROR

2007

### TABLE 3: CONVERGENCE BEHAVIOR IN THE PRESENCE OF STRONG INCOME EFFECTS

2007

### Table 1 - Span of a CREW tree for different levels of decomposition

1995

"... In PAGE 6: .... Zandi, M. Boliek, E.L. Schwartz, M.J. Gormish, A. Keith - 12 September 1995 v List of tables Table1 - Span of a CREW tree for different levels of decomposition.... In PAGE 17: ... For example, with one level of decomposition a CREW tree spans four pixels, with two levels it spans 16, etc. Table1 shows the number of pixels affected by a CREW tree for different levels. 1.... ..."

Cited by 7