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Multiple kernel learning refers to a set of machine learning methods that use a predefined set of kernels and learn an optimal linear or non-linear combination of kernels as part of the algorithm. Reasons to use multiple kernel learning include a) the ability to select for an optimal kernel and parameters from a larger set of kernels, reducing ...
Michael Alexander Kirkwood Halliday (often M. A. K. Halliday; 13 April 1925 – 15 April 2018) was a British linguist who developed the internationally influential systemic functional linguistics (SFL) model of language.
Decision trees are a popular method for various machine learning tasks. Tree learning is almost "an off-the-shelf procedure for data mining", say Hastie et al., "because it is invariant under scaling and various other transformations of feature values, is robust to inclusion of irrelevant features, and produces inspectable models.
Kolb's learning style is explained on the basis of two dimensions: they are how a person understands and processes the information. This perceived information is then classified as concrete experience or abstract conceptualization, and processed information as active experimentation or reflective observation.
Malcolm Shepherd Knowles (August 24, 1913 – November 27, 1997) was an American adult educator, famous for the adoption of the theory of andragogy—initially a term coined by the German teacher Alexander Kapp.
In computational learning theory, probably approximately correct (PAC) learning is a framework for mathematical analysis of machine learning. It was proposed in 1984 by Leslie Valiant . [ 1 ]
Robert E. Page Jr. (born 12 November 1949) is one of the foremost honey bee geneticists in the world and a Foundation Chair of Life Sciences of Arizona State University.An author of more than 250 research papers and articles, his work on the self-organizing regulatory networks of honey bees has been outlined in his book, "The Spirit of the Hive: The Mechanisms of Social Evolution," [1] [2 ...
Let be an binary matrix indicating the presence or absence of a latent feature. The IBP places the following prior on : = + = ()! {} = + ()!()!! where is the number of non-zero columns in , is the number of ones in column of , is the -th harmonic number, and () is the number of new dishes sampled by the -th customer.