Results 71 to 80 of about 8,068,470 (297)
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Progression independent of relapse activity is a major determinant of long‐term disability in multiple sclerosis, but its immunopathologic basis remains incompletely understood. We investigated whether relapse‐independent progression in radiologically stable relapsing–remitting multiple sclerosis is associated with distinct ...
Antonio Bruno +19 more
wiley +1 more source
SensorsThe annotation of magnetic resonance imaging (MRI) images plays an important role in deep learning-based MRI segmentation tasks. Semi-automatic annotation algorithms are helpful for improving the efficiency and reducing the difficulty of MRI image ...
Shaolong Chen, Zhiyong Zhang
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Geostatistical semi-supervised learning for spatial prediction
Artificial Intelligence in Geosciences, 2022 Geoscientists are increasingly tasked with spatially predicting a target variable in the presence of auxiliary information using supervised machine learning algorithms.
Francky Fouedjio, Hassan Talebi
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Minimally-Supervised Morphological Segmentation using Adaptor Grammars [PDF]
, 2013 This paper explores the use of Adaptor Grammars, a nonparametric Bayesian modelling framework, for minimally supervised morphological segmentation. We compare three training methods: unsupervised training, semi-supervised training, and a novel model ...
Sirts, Kairit +1 more
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Semi-Supervised Learning strategy.
, 2016 Semi-Supervised Learning strategy.
Haifeng Li (142063) +6 more
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Arthritis Care &Research, Accepted Article.Introduction Systemic sclerosis (SSc) is characterized by cardiovascular risk excess not fully explained by traditional factors. Whether the severity of microvascular damage correlates with structural subclinical atherosclerosis remains unclear. We investigated the relationship between nailfold videocapillaroscopy (NVC) abnormalities and carotid ...
Eugenio Capparelli +13 more
wiley +1 more source
A discriminative model for semi-supervised learning [PDF]
Journal of the ACM, 2010 Supervised learning—that is, learning from labeled examples—is an area of Machine Learning that has reached substantial maturity. It has generated general-purpose and practically successful algorithms and the foundations are quite well understood and captured by theoretical frameworks such as the PAC-learning model and the Statistical ...
Maria-Florina Balcan, Avrim Blum
openaire +1 more source
Semi-Supervised Neural Gas for Adaptive Brain-Computer Interfaces [PDF]
, 2012 Riechmann H, Finke A. Semi-Supervised Neural Gas for Adaptive Brain-Computer Interfaces. In: ESANN 2012 proceedings. i6doc.com; 2012: 121-126.Non-stationarity is inherent in EEG data.
Riechmann, Hannes +1 more
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A Knowledge‐Based Approach for Understanding and Managing Additive Manufacturing Data
Advanced Engineering Materials, EarlyView.Additive manufacturing processes generate a large amount of data. Effectively managing, understanding, and retrieving information from this data remains a major challenge. Therefore, we propose an ontology‐based approach to integrate heterogeneous data, enable semantic queries, and support decision‐making.
Mina Abd Nikooie Pour +5 more
wiley +1 more source
Deceptive Reviews Detection Based on Semi supervised Learning Algorithm
工程科学与技术, 2014 :Machine learning methods were presented to identify deceptive reviews.With the integration of knowledge from computational linguistics and psycholinguistics,supervised method was developed to evaluate the performance of different feature modelings,and ...
任亚峰, 姬东鸿, 尹兰
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