Издательство: | Книга по требованию |
Дата выхода: | июль 2011 |
ISBN: | 978-6-1304-9225-0 |
Объём: | 88 страниц |
Масса: | 153 г |
Размеры(В x Ш x Т), см: | 23 x 16 x 1 |
High Quality Content by WIKIPEDIA articles! Neighbourhood components analysis is an unsupervised learning method for clustering multivariate data into distinct classes according to a given distance metric over the data. Functionally, it serves the same purposes as the k-Nearest Neighbour algorithm, and makes direct use of a related concept termed stochastic nearest neighbours. Neighbourhood components analysis aims at "learning" a distance metric by finding a linear transformation of input data such that the average LOO-classification performance is maximized in the transformed space. The key insight to the algorithm is that a matrix A corresponding to the transformation can be found by defining a differentiable objective function for A, followed by use of an iterative solver such as conjugate gradient descent. One of the benefits of this algorithm is that the number of classes k can be determined as a function of A, up to a scalar constant. This use of the algorithm therefore addresses the issue of model selection.
Данное издание не является оригинальным. Книга печатается по технологии принт-он-деманд после получения заказа.