Matches in DBpedia 2015-04 for { <http://dbpedia.org/resource/T-distributed_stochastic_neighbor_embedding> ?p ?o }
Showing triples 1 to 12 of
12
with 100 triples per page.
- T-distributed_stochastic_neighbor_embedding abstract "t-distributed stochastic neighbor embedding (t-SNE) is a machine learning algorithm for dimensionality reduction developed by Laurens van der Maaten and Geoffrey Hinton. It is a nonlinear dimensionality reduction technique that is particularly well suited for embedding high-dimensional data into a space of two or three dimensions, which can then be visualized in a scatter plot. Specifically, it models each high-dimensional object by a two- or three-dimensional point in such a way that similar objects are modeled by nearby points and dissimilar objects are modeled by distant points.The t-SNE algorithms comprises two main stages. First, t-SNE constructs a probability distribution over pairs of high-dimensional objects in such a way that similar objects have a high probability of being picked, whilst dissimilar points have an infinitesimal probability of being picked. Second, t-SNE defines a similar probability distribution over the points in the low-dimensional map, and it minimizes the Kullback–Leibler divergence between the two distributions with respect to the locations of the points in the map.t-SNE has been used in a wide range of applications, including computer security research, music analysis, cancer research, and bio-informatics.".
- T-distributed_stochastic_neighbor_embedding wikiPageExternalLink t-SNE.html.
- T-distributed_stochastic_neighbor_embedding wikiPageID "39758474".
- T-distributed_stochastic_neighbor_embedding wikiPageRevisionID "629879583".
- T-distributed_stochastic_neighbor_embedding subject Category:Machine_learning_algorithms.
- T-distributed_stochastic_neighbor_embedding comment "t-distributed stochastic neighbor embedding (t-SNE) is a machine learning algorithm for dimensionality reduction developed by Laurens van der Maaten and Geoffrey Hinton. It is a nonlinear dimensionality reduction technique that is particularly well suited for embedding high-dimensional data into a space of two or three dimensions, which can then be visualized in a scatter plot.".
- T-distributed_stochastic_neighbor_embedding label "T-distributed stochastic neighbor embedding".
- T-distributed_stochastic_neighbor_embedding sameAs m.0w32q7h.
- T-distributed_stochastic_neighbor_embedding sameAs Q18387205.
- T-distributed_stochastic_neighbor_embedding sameAs Q18387205.
- T-distributed_stochastic_neighbor_embedding wasDerivedFrom T-distributed_stochastic_neighbor_embedding?oldid=629879583.
- T-distributed_stochastic_neighbor_embedding isPrimaryTopicOf T-distributed_stochastic_neighbor_embedding.