Results 281 to 290 of about 752,283 (330)

Supervised Classification Techniques

1986
Supervised classification is the technique most often used for the quantitative analysis of remote sensing image data. At its core is the concept of segmenting the spectral domain into regions that can be associated with the ground cover classes of interest to a particular application. In practice those regions may sometimes overlap.
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Supervised Learning for Classification

2005
Supervised local tangent space alignment is proposed for data classification in this paper. It is an extension of local tangent space alignment, for short, LTSA, from unsupervised to supervised learning. Supervised LTSA is a supervised dimension reduction method.
Hongyu Li, Wenbin Chen, I-Fan Shen
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Supervised Machine Learning—Classification

2017
Classification and prediction are two important methods of data analysis used to find patterns in data. Classification predicts the categorical class (or discrete values), whereas regression and other models predict continuous valued functions. For example, a classification model may be built to predict the results of a credit-card application approval
Umesh R. Hodeghatta, Umesh Nayak
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Supervised Classification

2018
Stacy A. C. Nelson, Siamak Khorram
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Weakly Supervised Text Classification

2019
Deep neural networks are gaining increasing popularity for the classic text classification task, due to their strong expressive power and less requirement for feature engineering. Despite such attractiveness, neural text classification models suffer from the lack of training data in many real-world applications. Although many semi-supervised and weakly-
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Supervised Classification

2022
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Supervised Classification

2021
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Classification–Supervised Learning

2021
J.S. Marron, Ian L. Dryden
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Supervised Classification

2008
André Oberthür, Patrick Warnat
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Weakly supervised group classification

Вычислительные технологии
В работе решается задача слабо-контролируемого обучения в постановке групповой бинарной классификации. Предполагается, что каждый объект выборки может включать набор подобъектов, относящихся к разным классам. Предлагаемый метод решения основан на выборе информативного признакового пространства и фильтрации обучающей выборки.
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