Given the notations of all superpixels, we initially train the superpixel-level classifier based on appearance features which consist of color and gradient statistic in each superpixel.
We use the following notation: set of all positions of true scene cuts in a test video sequence, set of all positions of scene cuts returned by the system, subset of that are true scene cuts (i.e., correct detection, or ).
Whereas the earlier decision-making approach sought to explain the decisions of elite groups (mostly in matters of foreign policy), rational choice theorists attempted to apply their far more formal theory (which sometimes involved the use of mathematical notation) to all facets of political life.
For brevity of notation, for all symbols in the group of packets, we rewrite (1) using the packet version of this relationship as: (2).
I think it would also be important to introduce a careful definition of the model, introducing notation for all parameters and observations at one place.
Notation The space of all infinitely differentiable functions f ( x, t ) with compact support in R × [ 0, + ∞ ) is denoted by C 0 ∞. Let p be any constant with 1 ≤ p < ∞ and denote L p = L p ( R ) to be the space of all measurable functions f such that ∥ f ∥ L p p = ∫ R | f ( x ) | p d x < ∞. The space L ∞ = L ∞ ( R ) with the standard norm ∥ f ∥ L ∞ = inf m ( e ) = 0 sup x ∈ R / e | f ( x ) |.
Suppose X is a random variable that can assume one of the values x1, x2,…, xm, according to the outcome of a random experiment, and consider the event {X = xi}, which is a shorthand notation for the set of all experimental outcomes e such that X e) = xi.
Let us introduce the notation for the prices of all traded assets in our model.
The notation denotes the class of all matrices A such that A : μ → γ.
The notation is the set of all absolutely integrable matrices for all, and if, we write instead of.
