Results 101 to 110 of about 7,571,754 (304)
Multi-label learning by exploiting label dependency
, 2010 In multi-label learning, each training example is associated with a set of labels and the task is to predict the proper label set for the unseen example.
Min-ling Zhang +3 more
core +2 more sources
Multi-Label Learning with Deep Forest
, 2020 In multi-label learning, each instance is associated with multiple labels and the crucial task is how to leverage label correlations in building models. Deep neural network methods usually jointly embed the feature and label information into a latent space to exploit label correlations.
Liang Yang +3 more
openaire +3 more sources
Arterial Spin‐Labeling MRI at the Cortical‐CSF Interface: A Novel Biomarker in Alzheimer Disease
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Background/Objective Arterial spin‐labeling (ASL) MRI can measure perfusion signal adjacent to CSF spaces and may provide information regarding CSF‐adjacent water transport physiology. We developed an automated pipeline to extract cortical‐CSF interface (IF) perfusion for comparison between Alzheimer disease (AD) and cognitively normal ...
Mona Asghariahmadabad +22 more
wiley +1 more source
MEKA: A multi-label/multi-target extension to WEKA [PDF]
, 2016 Multi-label classification has rapidly attracted interest in the machine learning literature, and there are now a large number and considerable variety of methods for this type of learning. We present MEKA: an open-source Java framework based on the well-
Pfahringer, Bernhard +4 more
core +2 more sources
IET Computer Vision, 2018 The classification of natural scene images is multi‐instance multi‐label (MIML) for many labels that exist in a natural scene image. The traditional method of solving MIML is to degenerate it into single‐instance single‐label learning (SISL).
Hu Zhang, Wei Wu, Ding Wang
doaj +1 more source
Multi-Label Event-Prediction Model Based on Event-Evolution Graph [PDF]
Jisuanji gongchengMultilabel event prediction refers to the prediction of whether multiple associated events will occur in the future, which requires the simultaneous prediction of multiple target events and comparing it with the conventional single-label event prediction.
WANG Huazhen, XU Ze, SUN Yue, QIU Bin, CHEN Jian, QIU Qiangbin
doaj +1 more source
White Matter and Perivascular Imaging Changes in Alzheimer's Disease and Cerebral Amyloid Angiopathy
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Peak‐width of skeletonized mean diffusivity (PSMD) and diffusion tensor imaging–analysis along the perivascular space (DTI‐ALPS), reflecting white matter integrity and glymphatic function, are altered in Alzheimer's disease (AD).
Debina Laishram +3 more
wiley +1 more source
Learning Multimodal Latent Attributes [PDF]
, 2014 —The rapid development of social media sharing has created a huge demand for automatic media classification and annotation techniques. Attribute learning has emerged as a promising paradigm for bridging the semantic gap and addressing data sparsity via ...
Yanwei Fu +7 more
core +1 more source
Functional inference in FunCat through the combination of hierarchical ensembles with data fusion methods [PDF]
, 2010 The multi-label hierarchical prediction of gene functions at genome and ontology-wide level is a central problem in bioinformatics, and raises challenging questions from a machine learning standpoint.
M. Re, N. Cesa-Bianchi, G. Valentini
core +1 more source
Arthritis Care &Research, Accepted Article.Biomedical research involving United States Veterans continues to advance healthcare beyond the Veterans Health Administration. This is particularly true in rheumatoid arthritis (RA), where Veteran‐centric research has uncovered novel insights into pathogenesis, risk factors, and disease manifestations, informing clinical care and research across both ...
Austin M. Wheeler +20 more
wiley +1 more source