Results 211 to 220 of about 38,536 (261)

DeepDRP: Dose-response predictions of drug pairs using deep learning based on data-driven feature representation and dose-response curve characteristics. [PDF]

PLoS One
Moragheb M, Dehghan A, Razzaghi P, Gharaghani S.   +3 more
europepmc   +1 more source

A Vision Language-Based Framework for Detecting Industrial Mechanical, Electrical, and Plumbing Assets Using Unlabelled Data. [PDF]

Sensors (Basel)
Kamali M, Atazadeh B, Rajabifard A, Chen Y, Javaherian Pour E.   +4 more
europepmc   +1 more source

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Safety-Aware Semi-Supervised Classification

IEEE Transactions on Neural Networks and Learning Systems, 2013
Though semi-supervised classification learning has attracted great attention over past decades, semi-supervised classification methods may show worse performance than their supervised counterparts in some cases, consequently reducing their confidence in real applications.
Songcan Chen, Yunyun Wang
exaly   +3 more sources

Semi-supervised classification trees

Journal of Intelligent Information Systems, 2017
In many real-life problems, obtaining labelled data can be a very expensive and laborious task, while unlabeled data can be abundant. The availability of labeled data can seriously limit the performance of supervised learning methods. Here, we propose a semi-supervised classification tree induction algorithm that can exploit both the labelled and ...
Jurica Levatic, Michelangelo Ceci, Dragi Kocev, Saso Dzeroski   +3 more
openaire   +2 more sources

Semi-supervised ensemble classification in subspaces

Applied Soft Computing Journal, 2012
Graph-based semi-supervised classification depends on a well-structured graph. However, it is difficult to construct a graph that faithfully reflects the underlying structure of data distribution, especially for data with a high dimensional representation.
Zhiwen Yu, Guoxian Yu, Jane You
exaly   +2 more sources

SEMI-SUPERVISED CLASSIFICATION USING BRIDGING

International Journal on Artificial Intelligence Tools, 2008
Traditional supervised classification algorithms require a large number of labelled examples to perform accurately. Semi-supervised classification algorithms attempt to overcome this major limitation by also using unlabelled examples. Unlabelled examples have also been used to improve nearest neighbour text classification in a method called bridging ...
Jason Chan 0001, Irena Koprinska, Josiah Poon   +2 more
openaire   +2 more sources

Semi-supervised classification by discriminative regularization

Applied Soft Computing, 2017
Abstract One basic assumption in graph-based semi-supervised classification is manifold assumption, which assumes nearby samples should have similar outputs (or labels). However, manifold assumption may not always hold for samples lying nearby but across the boundary of different classes.
Jun Wang 0035, Guangjun Yao, Guoxian Yu
openaire   +1 more source