enow.com Web Search

Search results

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

    en.wikipedia.org/wiki/Unsupervised_learning

    Unsupervised learning is a framework in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled data. [1] Other frameworks in the spectrum of supervisions include weak- or semi-supervision , where a small portion of the data is tagged, and self-supervision .

  3. Competitive learning - Wikipedia

    en.wikipedia.org/wiki/Competitive_learning

    Competitive learning is a form of unsupervised learning in artificial neural networks, in which nodes compete for the right to respond to a subset of the input data. [ 1 ] [ 2 ] A variant of Hebbian learning , competitive learning works by increasing the specialization of each node in the network.

  4. Generalized Hebbian algorithm - Wikipedia

    en.wikipedia.org/wiki/Generalized_Hebbian_algorithm

    The generalized Hebbian algorithm is an iterative algorithm to find the highest principal component vectors, in an algorithmic form that resembles unsupervised Hebbian learning in neural networks. Consider a one-layered neural network with n {\displaystyle n} input neurons and m {\displaystyle m} output neurons y 1 , … , y m {\displaystyle y ...

  5. Self-organizing map - Wikipedia

    en.wikipedia.org/wiki/Self-organizing_map

    The examples are usually administered several times as iterations. The training utilizes competitive learning. When a training example is fed to the network, its Euclidean distance to all weight vectors is computed. The neuron whose weight vector is most similar to the input is called the best matching unit (BMU). The weights of the BMU and ...

  6. Pattern recognition - Wikipedia

    en.wikipedia.org/wiki/Pattern_recognition

    An example of pattern recognition is classification, which attempts to assign each input value to one of a given set of classes (for example, determine whether a given email is "spam"). Pattern recognition is a more general problem that encompasses other types of output as well.

  7. List of datasets for machine-learning research - Wikipedia

    en.wikipedia.org/wiki/List_of_datasets_for...

    High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to produce because of the large amount of time needed to label the data. Although they do not need to be labeled, high-quality datasets for unsupervised learning can also be difficult and costly to produce ...

  8. Conceptual clustering - Wikipedia

    en.wikipedia.org/wiki/Conceptual_clustering

    For example, we might permit only concepts wherein at least one probability differs from 0.5 by more than . Under this constraint, with α = 0.3 {\displaystyle \alpha =0.3} , a concept such as [.6 .5 .7] could not be constructed by the learner; however a concept such as [.6 .5 .9] would be accessible because at least one probability differs ...

  9. Deep belief network - Wikipedia

    en.wikipedia.org/wiki/Deep_belief_network

    The observation [2] that DBNs can be trained greedily, one layer at a time, led to one of the first effective deep learning algorithms. [4]: 6 Overall, there are many attractive implementations and uses of DBNs in real-life applications and scenarios (e.g., electroencephalography, [5] drug discovery [6] [7] [8]).