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In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers. A binary classifier is a function which can decide whether or not an input, represented by a vector of numbers, belongs to some specific class. [ 1 ]
If a multilayer perceptron has a linear activation function in all neurons, that is, a linear function that maps the weighted inputs to the output of each neuron, then linear algebra shows that any number of layers can be reduced to a two-layer input-output model.
In machine learning, the kernel perceptron is a variant of the popular perceptron learning algorithm that can learn kernel machines, i.e. non-linear classifiers that employ a kernel function to compute the similarity of unseen samples to training samples. The algorithm was invented in 1964, [1] making it the first kernel classification learner. [2]
The problem with polynomials may be removed by allowing the outputs of the hidden layers to be multiplied together (the "pi-sigma networks"), yielding the generalization: [41] Universal approximation theorem for pi-sigma networks — With any nonconstant activation function, a one-hidden-layer pi-sigma network is a universal approximator.
The Gamba perceptron machine was similar to the perceptron machine of Rosenblatt. Its input were images. The image is passed through binary masks (randomly generated) in parallel. Behind each mask is a photoreceiver that fires if the input, after masking, is bright enough. The second layer is made of standard perceptron units.
A multilayer perceptron (MLP) is a misnomer for a modern feedforward artificial neural network, consisting of fully connected neurons (hence the synonym sometimes used of fully connected network (FCN)), often with a nonlinear kind of activation function, organized in at least three layers, notable for being able to distinguish data that is not ...
The perceptron uses the Heaviside step function as the activation function (), and that means that ′ does not exist at zero, and is equal to zero elsewhere, which makes the direct application of the delta rule impossible.
Empirical risk minimization for a classification problem with a 0-1 loss function is known to be an NP-hard problem even for a relatively simple class of functions such as linear classifiers. [5] Nevertheless, it can be solved efficiently when the minimal empirical risk is zero, i.e., data is linearly separable .