Results 251 to 260 of about 7,571,754 (304)

Federated multi-label text feature selection via manifold-aware sparse modeling and cooperative grey wolf optimization. [PDF]

Sci Rep
Zheng Y, Ye Z, Zhang S, Wang K.
europepmc   +1 more source

Benchmarking MedViT and hybrid CNN-ViT architectures for multi-label thoracic disease classification. [PDF]

Sci Rep
Agbo V, Patel R, Lunagaria M, Malviya L, Jadhav A, Monga S.   +5 more
europepmc   +1 more source

Standard knee radiographs enable deep learning inference of MRI-defined cartilage and meniscal damage in early knee osteoarthritis: a study using the osteoarthritis initiative database. [PDF]

Front Physiol
Alkhatatbeh T, Alkhatatbeh A, Liao H, Liao Y, Zhang Z, Fang H, Chen W, Wu D, Zhang R.   +8 more
europepmc   +1 more source

Label tree semantic losses for rich multi-class medical image segmentation. [PDF]

Front Artif Intell
Wang J, MacCormac O, Rochford W, Kujawa A, Shapey J, Vercauteren T.   +5 more
europepmc   +1 more source

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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A Review on Multi-Label Learning Algorithms

IEEE Transactions on Knowledge and Data Engineering, 2014
Multi-label learning studies the problem where each example is represented by a single instance while associated with a set of labels simultaneously. During the past decade, significant amount of progresses have been made toward this emerging machine learning paradigm.
Zhi-Hua Zhou, Min-Ling Zhang
exaly   +2 more sources

Multi-label Rule Learning [PDF]

, 2022
Forschung im Bereich der Multi-label Klassifizierung beschäftigt sich mit der Entwicklung und Bewertung von Algorithmen, die Vorhersagemodelle für die automatische Zuweisung von Datenpunkten zu einer Untermenge vordefinierter Klassen lernen. Dies unterscheidet sich von traditionellen Problemstellungen, die es nicht erlauben, einzelne Datenpunkte mehr ...
Rapp, Michael
openaire   +3 more sources

Multi-Label Learning Via Codewords

2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI), 2018
In this paper, we introduce a novel hash learning framework for multi-label learning which employs structured prediction. A hash function is learned to embed samples in Hamming spaces, and for each label, a pair of codewords are simultaneously inferred from the available data.
Sedghi, Mahlagha, Huang, Yinjie, Georgiopoulos, Michael, Anagnostopoulos, Georgios   +3 more
openaire   +3 more sources