Designed for simplicity, this example features a simple mobile robot, passive tools installed on existing appliances dispersed through the room, and a motor mounted on the mobile robot, which delivers the sole power supply.
This example features some of Ganesha's common iconographic elements.
This example features an IDE drive.
Elements of the training set are usually represented as a collection of values for prespecified features or attributes; for this example, these features could be such measurable properties as age, recent hospitalizations, recent clinic visits, etc. Training set elements are marked a priori with their outcome, or class membership (e.g., "high risk").
The guided search theory argues that top-down set can increase the salience of the relevant feature dimension (in this example: the feature "red" or "green") so that attention is (mostly) guided to relevant features only (Wolfe, 1994; Wolfe, Butcher, Lee, & Hyle, 2003).
Guided search argues that a top-down set can increase the salience of the relevant feature dimension (in this example the feature "red" or "green") so that attention is (mostly) guided to relevant features only (Wolfe, 1994; Wolfe et al., 2003).
Similarly, we find that the optimal set in this example contains features 1, 5, 6, 7, and 8, with an associated error rate of 13%.
H, for example, features 12 American heroes.
One hole, for example, features a ramp covered with suitcases.
A so-called avenue bower, for example, features two parallel walls of sticks.
"Observation," for example, features Columbus and space exploration, while including Audubon, anatomy and cartography.
