Tools
Sorted by:
Try your query at:
 |
 |
 |
 |
 |
 |
Results 1 - 10
of
18,267
Table AI.8 Unemployment Rates by Position in Household 1996 1999 2000
TABLE I DEFINITION OF THE MOST IMPORTANT SPL CONSTRUCTS IN BACKUS-NAUR FORM; n;k ARE POSITIVE INTEGERS, ;ai REAL NUMBERS.
Table 3: The supports ( ai) of amino acid positions for the formation of -turns (turn-windows) under the linear classi cation model of BTSVM Lin. Amino acid positions with positive supports will contribute to the formation of -turn, others will prevent the formation of -turn. The larger the absolute value of the support, the stronger the contribution (or prevention if negative). Amino acid positions with the strongest supports (more than 0.50) are printed by boldface. Those with the lowest supports (less than -0.50) are underlined.
Table 5.1: Parameters used to define the reference solutions. All of these values give rise to results between 0 and 40. The performance parameters in this set of tests are the coefficients ai, and the radius rj used to perturb the nodal positions.
2005
Table 3: The experimental result (chi-square). AI 0 102030405060 Precision/Recall Positive 76.4/92.4 84.0/86.7 84.1/83.5 86.2/79.1 88.7/74.7 86.7/65.8 86.7/65.8
2007
"... In PAGE 7: ... By changing the threshold AI, we investigated recall- precision curve (Figure 6 and 7). The detail is rep- resented in Table3 and 4. The second/third row represents precision and recall of positive/negative phrases.... ..."
Cited by 1
Table 3: The experimental result (chi-square). AI 0 102030405060 Precision/Recall Positive 76.4/92.4 84.0/86.7 84.1/83.5 86.2/79.1 88.7/74.7 86.7/65.8 86.7/65.8
2007
"... In PAGE 7: ... By changing the threshold AI, we investigated recall- precision curve (Figure 6 and 7). The detail is rep- resented in Table3 and 4. The second/third row represents precision and recall of positive/negative phrases.... ..."
Cited by 1
TABLE 3 Additional axioms for BVBF-closure of positive constraints B3 over nonempty intersection variables DCCB where CB AI CCCTD6D1D7B4B3B5
1997
Cited by 29
Table 1: A behavioural look-up table for Beast1. The subscript s indicates a sensor and the subscript m indicates a motor When we place the beast in a real-world environment we say that it is situated. The proximal description of such a situated robot consists not just of sensor and motor states, but also of a movement. A single sensor state resulting in a motor state resulting in a movement is called a sensory-motor loop. The movement is important in the proximal description in that it may result in new sensory states (this doesn apos;t happen with the robot on its back on the table), which, in turn, result in new sensory-motor loops. An example of a sensory-motor loop is 100-p, where `100 apos; is a sensory state indicating that the port light sensor is on (See 2This is reminiscent of the strong AI position of John McCarthy of Stanford University who says that the thermostat in his house has three quot;beliefs quot;: its too hot in here, its too cold in here, and the temperature is just right in here.
1997
"... In PAGE 6: ...Table1 ) and quot;p quot; indicates a turn to port. However, this proximal description of robot behaviour is quite unlike that used by the experimenter observing Beast1.... In PAGE 7: ... We can think of the nervous system, or, more properly, the control system of Beast1 as implementing two Boolean functions using two McCulloch-Pitts nets; one function for each output/motor unit. An examination of Table1 shows that these functions are P amp; H for the output to the Port motor, and simply S for the output to the Starboard motor. By building up large networks made up of smaller McCulloch-Pitts networks it would be possible to develop a much larger and more complex arti cial neural brain capable of leading our Beast through many great feats of singing and dancing.... In PAGE 8: ...Table1 , the sensor states 001 will result in output motor states of 00. But of course the ever-following designer can jump in now and connect a new positive wire between the heat sensor and the starboard motor.... In PAGE 9: ..., 1993)) was trained on obstacle avoidance and goal nding. In the experiments, the distance sensor and motor values are all continuously valued thus a binary lookup table such as in Table1 will no longer su ce. In order to learn the mapping between sensors and actuators for avoiding obstacles the robot was given a simple re ex behaviour of quot;move away if an object gets too close quot;.... ..."
Cited by 18
(Table 2.): based on other papers [9,10,16] and different discussions. The second column indicates whether the different AI techniques (mainly rule base systems, neural nets and fuzzy logic) would provide methods and solutions. One can find positive answers to all these issues in the resent literature:
2001
Cited by 1
Table 1.Polynomial coefficients for the action potential model iiai ai
2005
"... In PAGE 10: ... This was due to the fact that, when the cell is triggered, the signal generated by the Hodgkin-Huxley model is nonlinear, as we can see in Fig- ure 12.Thus, between 0 and 2 msec were needed to approx- imate the action potential using four different polynomials (as shown in Table1 ). We also introduced an intermediate state in which the polynomial evaluation would result in obtaining a positive value, which will trigger activity in the neighboring cells in this example (polynomial P2isin charge of this).... ..."
Cited by 4
Results 1 - 10
of
18,267
