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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 .
For example, 12.380 becomes the integer 12380 by multiplying by 1000. This must of course be taken into account in genotype-phenotype mapping for evaluation and result presentation. A common form is a chromosome consisting of a list or an array of integer or real values.
With the theory of virtual alphabets, David E. Goldberg showed in 1990 that by using a representation with real numbers, an EA that uses classical recombination operators (e.g. uniform or n-point crossover) cannot reach certain areas of the search space, in contrast to a coding with binary numbers. [23]
Examples include artificial neural networks, multilayer perceptrons, and supervised dictionary learning. In unsupervised feature learning, features are learned with unlabeled input data. Examples include dictionary learning, independent component analysis, autoencoders, matrix factorization [62] and various forms of clustering. [63] [64] [65]
The list of organisms by chromosome count describes ploidy or numbers of chromosomes in the cells of various plants, animals, protists, and other living organisms. This number, along with the visual appearance of the chromosome, is known as the karyotype , [ 1 ] [ 2 ] [ 3 ] and can be found by looking at the chromosomes through a microscope .
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 learning process consists of two stages: The spatial pooling identifies (in the input) frequently observed patterns and memorise them as "coincidences". Patterns that are significantly similar to each other are treated as the same coincidence. A large number of possible input patterns are reduced to a manageable number of known coincidences.
In general, a machine learning system can usually be trained to recognize elements of a certain class given sufficient samples. [30] For example, machine learning methods can be trained to identify specific visual features such as splice sites. [31] Support vector machines have been extensively used in cancer genomic studies. [32]