### Table 1: Complexity and Space Efficiency of Formalisms

1996

Cited by 7

### Table 1: Complexity of model checking and Space Efficiency of Model Representations

1996

Cited by 26

### Table 2: Complexity of inference and Space Efficiency of Theorem Representations

1996

Cited by 26

### Table 1: Complexity of model checking and Space Efficiency of Model Representations

1996

Cited by 6

### Table 2: Complexity of inference and Space Efficiency of Theorem Representations

1996

Cited by 6

### Table 1 shows the performance of the various methods for the five affective states under investigation. The classifiers have different overall performances for different kinds of emotions. For example, classification accuracy is consistently better for anxiety than for frustration. One possible reason could be that the task design resulted in elicitation of particular emotions more successfully than the others. Efficiency: Real-time embedded applications in robotics require time and space efficiency of the learning algorithms

in An Empirical Study of Machine Learning Techniques for Affect Recognition in Human-Robot Interaction

2006

Cited by 5

### Table 1. Comparison of DCFA*, Sweep A* using a sub-optimal upper bound found by modified RTA*, and Sweep A* using an optimal upper bound. Results are for aligning 3 random sequences of length 4000 through 8000. The comparison shows the number of nodes expanded during the cost-only pass, in thousands (Exp); the max- imum number of nodes stored at any time, in thousands (Stored); and the running time in CPU seconds (Secs).

2003

"... In PAGE 6: ... As a re- sult, no general-purpose search algorithm has been shown to be as space-efficient as DCFA*. Table1 compares the performance of DCFA* to Sweep A*. Results are shown for Sweep A* using both a sub-optimal upper bound (com- puted by our modified RTA* algorithm) and using the cost of an optimal solution as an upper bound.... ..."

Cited by 13

### Table 1. Comparison of DCFA*, Sweep A* using a sub-optimal upper bound found by modified RTA*, and Sweep A* using an optimal upper bound. Results are for aligning 3 random sequences of length 4000 through 8000. The comparison shows the number of nodes expanded during the cost-only pass, in thousands (Exp); the max- imum number of nodes stored at any time, in thousands (Stored); and the running time in CPU seconds (Secs).

2003

"... In PAGE 6: ... As a re- sult, no general-purpose search algorithm has been shown to be as space-efficient as DCFA*. Table1 compares the performance of DCFA* to Sweep A*. Results are shown for Sweep A* using both a sub-optimal upper bound (com- puted by our modified RTA* algorithm) and using the cost of an optimal solution as an upper bound.... ..."

Cited by 13

### Table 3.2 shows that there is a significant gain in space and time efficiency when moving from A0 LR to A2LR. Apart from the dynamic costs of parsing, we have also measured some quantities relevant to the construction and storage of the two types of tabular LR parser. These data are given in Ta- ble 3.3. We see that the number of states is strongly reduced with regard to traditional LR parsing. In the case of the Alvey grammar, moving from jRLRj to jR2LRj amounts to a reduction to 20.3 %. Whereas time- and space-efficient computation of RLR for this grammar is a serious problem, computation of R2LR will not be difficult on any modern computer. Also significant is the reduc- tion from jT0 LRj to jT2LRj, especially for the larger grammars. These quantities correlate with the

### Table 42: Space Efficiency, Speedup, Power Consumption Saved (Configuration II) BTD4D4D0CXCRCPD8CXD3D2 CBD4CPCRCT CTAUCRCXCTD2CRDD CBD4CTCTCSD9D4

2007

"... In PAGE 76: ... The performance of the configurations has been evaluated using benchmark ap- plications. Table42 shows the space efficiency and speedup. Four out of five benchmark applications have space efficiency greater than 2.... ..."