Results 261 to 270 of about 3,113,449 (308)

A Practical Noise2Noise Denoising Pipeline for High‐Throughput Raman Spectroscopy

Advanced Engineering Materials, EarlyView.
A lightweight and reproducible denoising pipeline for high‐throughput Raman spectroscopy is introduced, based on a 1D convolutional autoencoder trained with a Noise2Noise strategy. Using only repeated short‐exposure acquisitions, the method suppresses stochastic noise without reference spectra, enabling reliable spectral reconstruction while preserving
David Martin‐Calle, Cesar Alvarez‐Llamas, Vincent Motto‐Ros, Christophe Dujardin, Jérémie Margueritat, David Rodney   +5 more
wiley   +1 more source

Multi-label Random Subspace Ensemble Classification. [PDF]

J Comput Graph Stat
Bi F, Zhu J, Feng Y.
europepmc   +1 more source

Acoustic Event Detection in Vehicles: A Multi-Label Classification Approach. [PDF]

Sensors (Basel)
Antony A, Theimer W, Grossetti G, Friedrich CM.   +3 more
europepmc   +1 more source

MvAl-MFP: A Multi-Label Classification Method on the Functions of Peptides with Multi-View Active Learning. [PDF]

Curr Issues Mol Biol
Peng Y, Duan J, Dan Y, Yu H.
europepmc   +1 more source

Assessing the Impact of Downsampled ECGs and Alternative Loss Functions in Multi-Label Classification of 12-Lead ECGs. [PDF]

Cardiovasc Eng Technol
Singstad BJ, Muten EM.
europepmc   +1 more source

A multi-omics integration framework using multi-label guided learning and multi-scale fusion. [PDF]

Brief Bioinform
Li Y, Wang Y, Liang T, Li Y, Du W.
europepmc   +1 more source

Multi-Label Classification

International Journal of Data Warehousing and Mining, 2007
Multi-label classification methods are increasingly required by modern applications, such as protein function classification, music categorization, and semantic scene classification. This article introduces the task of multi-label classification, organizes the sparse related literature into a structured presentation and performs comparative ...
Grigorios Tsoumakas, Ioannis Katakis 0001   +1 more
openaire   +2 more sources

Multi-Label Manifold Learning

Proceedings of the AAAI Conference on Artificial Intelligence, 2016
This paper gives an attempt to explore the manifold in the label space for multi-label learning. Traditional label space is logical, where no manifold exists. In order to study the label manifold, the label space should be extended to a Euclidean space.
Peng Hou, Xin Geng 0001, Min-Ling Zhang
openaire   +3 more sources

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Assessing the Multi-labelness of Multi-label Data

2020
Before constructing a classifier, we should examine the data to gain an understanding of the relationships between the variables, to assist with the design of the classifier. Using multi-label data requires us to examine the association between labels: its multi-labelness.
Laurence A. F. Park, Yi Guo 0001, Jesse Read   +2 more
openaire   +1 more source