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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 .
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.
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 ...
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 ...
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.
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 ...
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 ...
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]).