enow.com Web Search

Search results

  1. Results from the WOW.Com Content Network
  2. Perceptron - Wikipedia

    en.wikipedia.org/wiki/Perceptron

    The perceptron algorithm is also termed the single-layer perceptron, to distinguish it from a multilayer perceptron, which is a misnomer for a more complicated neural network. As a linear classifier, the single-layer perceptron is the simplest feedforward neural network .

  3. Kernel perceptron - Wikipedia

    en.wikipedia.org/wiki/Kernel_perceptron

    Plugging these two equations into the training loop turn it into the dual perceptron algorithm. Finally, we can replace the dot product in the dual perceptron by an arbitrary kernel function, to get the effect of a feature map Φ without computing Φ(x) explicitly for any samples. Doing this yields the kernel perceptron algorithm: [4]

  4. History of artificial neural networks - Wikipedia

    en.wikipedia.org/wiki/History_of_artificial...

    Artificial neural networks (ANNs) are models created using machine learning to perform a number of tasks.Their creation was inspired by biological neural circuitry. [1] [a] While some of the computational implementations ANNs relate to earlier discoveries in mathematics, the first implementation of ANNs was by psychologist Frank Rosenblatt, who developed the perceptron. [1]

  5. Mark I Perceptron - Wikipedia

    en.wikipedia.org/wiki/Mark_I_Perceptron

    The Mark I Perceptron was a pioneering supervised image classification learning system developed by Frank Rosenblatt in 1958. It was the first implementation of an Artificial Intelligence (AI) machine.

  6. Perceptrons (book) - Wikipedia

    en.wikipedia.org/wiki/Perceptrons_(book)

    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.

  7. Recurrent neural network - Wikipedia

    en.wikipedia.org/wiki/Recurrent_neural_network

    [18]: 73–75 Later, in Principles of Neurodynamics (1961), he described "closed-loop cross-coupled" and "back-coupled" perceptron networks, and made theoretical and experimental studies for Hebbian learning in these networks, [17]: Chapter 19, 21 and noted that a fully cross-coupled perceptron network is equivalent to an infinitely deep ...

  8. Delta rule - Wikipedia

    en.wikipedia.org/wiki/Delta_rule

    While the delta rule is similar to the perceptron's update rule, the derivation is different. The perceptron uses the Heaviside step function as the activation function g ( h ) {\\displaystyle g(h)} , and that means that g ′ ( h ) {\\displaystyle g'(h)} does not exist at zero, and is equal to zero elsewhere, which makes the direct application ...

  9. Feedforward neural network - Wikipedia

    en.wikipedia.org/wiki/Feedforward_neural_network

    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 ...