Search results
Results from the WOW.Com Content Network
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 .
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 ...
[5] [6] It was trained by an unsupervised learning algorithm. The LeNet-5 ( Yann LeCun et al., 1989) [ 7 ] [ 8 ] was trained by supervised learning with backpropagation algorithm, with an architecture that is essentially the same as AlexNet on a small scale.
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.
For example, XCS, [11] the best known and best studied LCS algorithm, is Michigan-style, was designed for reinforcement learning but can also perform supervised learning, applies incremental learning that can be either online or offline, applies accuracy-based fitness, and seeks to generate a complete action mapping.
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 ...
Algorithms for pattern recognition depend on the type of label output, on whether learning is supervised or unsupervised, and on whether the algorithm is statistical or non-statistical in nature. Statistical algorithms can further be categorized as generative or discriminative .
Pages in category "Unsupervised learning" The following 27 pages are in this category, out of 27 total. This list may not reflect recent changes. ...