Results 21 to 30 of about 38,536 (261)
Efficient Cancer Classification by Coupling Semi Supervised and Multiple Instance Learning
IEEE Access, 2022 The annotation of large datasets is often the bottleneck in the successful application of artificial intelligence in computational pathology. For this reason recently Multiple Instance Learning (MIL) and Semi Supervised Learning (SSL) approaches are ...
Arne Schmidt +3 more
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Semi-supervised Learning on Graphs Using Adversarial Training with Generated Sample [PDF]
Jisuanji kexue yu tansuo, 2023 Given a graph composed of a small number of labeled nodes and a large number of unlabeled nodes, semi-supervised learning on graphs aims to assign labels for the unlabeled nodes.
WANG Cong, WANG Jie, LIU Quanming, LIANG Jiye
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Semi-supervised morphosyntactic classification of Old Icelandic. [PDF]
PLoS ONE, 2014 We present IceMorph, a semi-supervised morphosyntactic analyzer of Old Icelandic. In addition to machine-read corpora and dictionaries, it applies a small set of declension prototypes to map corpus words to dictionary entries.
Kryztof Urban +3 more
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DE-ELM-SSC+：Semi-supervised Classification Algorithm
Jisuanji kexue yu tansuo, 2020 The combinations of evolutionary algorithms (EA) and analytical methods have been extensively studied in the fields of machine learning in recent years. This paper focuses on how to combine a differential evolution (DE) algorithm with the semi-supervised
PANG Jun, HUANG Heng, ZHANG Shou, SHU Zhiliang, ZHAO Yuhai
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SEMI-SUPERVISED SEQUENCE CLASSIFICATION WITH HMMs [PDF]
International Journal of Pattern Recognition and Artificial Intelligence, 2005 Using unlabeled data to help supervised learning has become an increasingly attractive methodology and proven to be effective in many applications. This paper applies semi-supervised classification algorithms, based on hidden Markov models, to classify sequences.
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Digging Into Pseudo Label: A Low-Budget Approach for Semi-Supervised Semantic Segmentation
IEEE Access, 2020 The capability to understand visual scenes with limited labeled data has been widely concerned in the field of computer vision. Although semi-supervised learning for image classification has been extensively studied in some cases, semantic segmentation ...
Zhenghao Chen +4 more
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Semi-Supervised DEGAN for Optical High-Resolution Remote Sensing Image Scene Classification
Remote Sensing, 2022 Semi-supervised methods have made remarkable achievements via utilizing unlabeled samples for optical high-resolution remote sensing scene classification.
Jia Li +4 more
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Milking CowMask for Semi-supervised Image Classification [PDF]
Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2022 Consistency regularization is a technique for semi-supervised learning that underlies a number of strong results for classification with few labeled data. It works by encouraging a learned model to be robust to perturbations on unlabeled data. Here, we present a novel mask-based augmentation method called CowMask.
Geoff French +2 more
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Semi-supervised generalized eigenvalues classification
Annals of Operations Research, 2017 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Marco Viola +3 more
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EURASIP Journal on Wireless Communications and Networking, 2020 With the advent of the era of network information, the amount of data in network information is getting larger and larger, and the classification of data becomes particularly important.
Yang Gang +5 more
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