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In graph theory, an outerplanar graph is a graph that has a planar drawing for which all vertices belong to the outer face of the drawing. Outerplanar graphs may be characterized (analogously to Wagner's theorem for planar graphs) by the two forbidden minors K 4 and K 2,3, or by their Colin de Verdière graph invariants. They have Hamiltonian ...
In machine learning, hyperparameter optimization [1] or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process, which must be configured before the process starts. [2] [3]
This tuning in the somatosensory system also provides feedback to the motor system so that it may selectively tune neurons to respond in specific ways to given stimuli. [9] Finally, the encoding and storage of information in both short-term and long-term memory requires the tuning of neurons in complex ways such that information may be later ...
The main approaches for stepwise regression are: Forward selection, which involves starting with no variables in the model, testing the addition of each variable using a chosen model fit criterion, adding the variable (if any) whose inclusion gives the most statistically significant improvement of the fit, and repeating this process until none improves the model to a statistically significant ...
Figure 5 is the Bode gain plot for the two-pole amplifier in the range of frequencies up to the second pole position. The assumption behind Figure 5 is that the frequency f 0 dB lies between the lowest pole at f 1 = 1/(2πτ 1) and the second pole at f 2 = 1/(2πτ 2). As indicated in Figure 5, this condition is satisfied for values of α ≥ 1.
In deep learning, fine-tuning is an approach to transfer learning in which the parameters of a pre-trained neural network model are trained on new data. [1] Fine-tuning can be done on the entire neural network, or on only a subset of its layers, in which case the layers that are not being fine-tuned are "frozen" (i.e., not changed during backpropagation). [2]
Or 15 seconds’ worth of it has, anyway, thanks to the tune taking off and becoming 2021’s unlikeliest TikTok sensation. In most of the viral videos made with the song as soundtrack, users do a ...
Ballester and Haeringer [3] proved the following two conditions are necessary and sufficient for a profile of preferences to be single-peaked preferences.. Worst-restricted: For every triplet of outcomes in X, there exists an outcome that is not ranked last by any agent in N.