Sentence examples for projection networks from inspiring English sources

The typical static neural networks are the recurrent back-propagation networks and the projection networks.

The same is true for the degree and strength distributions of the one-mode projection networks as well.

In addition, we use a weighted version of one-mode projections in this work, while the unweighted one-mode projection networks were considered in the previous work [ 25].

Table 1 lists the complexes and proteins with the ten largest degree and strength values in both the bipartite and projection networks.

For both bipartite and projection networks, the distributions approximately follow the exponential distribution, while most PINs in the literature are claimed to show more inhomogeneous degree distributions [ 4, 5, 13, 21].

The projection network was proven to be a viable alternative to existing methods.

This paper describes the development of the utility of a dynamic neural network known as projection network for pattern classification.

The procedures for utilizing the projection network for pattern classification are established and the benefits are discussed.

It first gives the derivation of the projection network, and then describes the network architecture and analyzes properties such as equilibrium points and their stability condition.

A comparative evaluation of the K-NN, fuzzy K-NN, conventional backpropagation network, and proposed neuro-fuzzy projection network classifiers is carried out.

Different roles of core and attachment proteins are also reflected in the clustering structure of complex-mode projection network.