This framework can be extended to multiple class situations by Kesler's construction. Cybenko and  Lippman showed how to create instances of a generalized type of these constructs that approximate arbitrary  continuous functions. One influential paper about these structures showed that the basic type of these cannot  solve connectedness problems such as XOR since they are only effective on linearly separable vectors. This
This framework can be extended to multiple class situations by Kesler's construction. Cybenko and  Lippman showed how to create instances of a generalized type of these constructs that approximate arbitrary  continuous functions. One influential paper about these structures showed that the basic type of these cannot  solve connectedness problems such as XOR since they are only effective on linearly separable vectors. This  framework is a real valued generalization of the McCulloch–Pitts model. Early models of this contained a starting  retina layer followed by an output layer and did not contain a hidden layer. This model was first proposed by Frank  Rosenblatt. Backpropagation networks are also known as multilayer this. In this model, an output signal is generated  if a weighted sum of the inputs exceeds a threshold. For 10 points, name this basic feed-forward neural network and  classifier.
This framework can be extended to multiple class situations by Kesler's construction.
single layer perceptron [or multilayer perceptron before connectedness]