Sentence examples for dimensional output from inspiring English sources

The network represents a map from M0 dimensional input space to N0 dimensional output space written as, S : R 0 M → R 0 N.

Namely, while in a colorless task, robots can always adopt each other outputs (δ can send simplexes to lower dimensional output simplexes), this is not possible in general tasks (δ sends a final state's algorithm simplex to an output simplex of the same dimension).

It should measure the proportion of all outputs and inputs for each of the DMUs, namely u ′ y i /v ′ x i, where u is the s × 1 dimensional output weight vector and v is the m × 1 dimensional input weight vector.

Fig. 8 Vessel retrieval results for four representations: the feature vectors of pre-trained VGG-F network (shown in magenta), AlexNet network based 109 (shown in blue), 4144 (shown in green) dimensional output based, and Siamese network (shown in orange) representations.

A CCR model presumes that there are n DMUs whereas each DMU has m types of input and s types of output, whereas vector xj and yj are used to represent the j-th DMU: the input vector xj = (x1j, x2j, …xmj Twhile the output vector yj = (y1j, y2j, …, ysj T, (i = 1,2,3,…n).. x represents the m × n dimensional input matrix and y represents the s × n dimensional output matrix.

The two dimensional output of the RBF network is: y i = W Φ (x i ) where the basis functions constituting Φ are selected using cross validation.

Experimental analysis on the sparsity and efficacy of low dimensional outputs shows that, sparse dimensionality reduction based on SELF can yield good classification results and interpretability in the field of hyperspectral remote sensing.

The second representation is the 4144 dimensional output-based generic description designed for distinguishing both vessel types and identities.

Being able to learn both, hence extracting both coarse and fine details, 4144 dimensional output-based representation is the best of three for generic vessel description.

Fig. 9 Vessel type specific recognition: Average recognition accuracies computed within each of the 29 vessel types on IMO testing set are depicted for extracted 109- (blue), 4035- (red), and 4144- (green) dimensional output-based representations and VGG-VD-19-based 4144-dimensional output-based representation (gray) learned in IMO training set.

But there is a sameness to all Mr. Burton's two- and three-dimensional output that makes for a monotonous viewing experience.