Results 31 to 40 of about 3,746,359 (297)
MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification [PDF]
Annual Meeting of the Association for Computational Linguistics, 2020 This paper presents MixText, a semi-supervised learning method for text classification, which uses our newly designed data augmentation method called TMix.
Jiaao Chen, Zichao Yang, Diyi Yang
semanticscholar +1 more source
Transferability and Hardness of Supervised Classification Tasks [PDF]
IEEE International Conference on Computer Vision, 2019 We propose a novel approach for estimating the difficulty and transferability of supervised classification tasks. Unlike previous work, our approach is solution agnostic and does not require or assume trained models.
A. Tran, Cuong V Nguyen, Tal Hassner
semanticscholar +1 more source
Self-supervised Vision Transformers for Land-cover Segmentation and Classification
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2022 Transformer models have recently approached or even surpassed the performance of ConvNets on computer vision tasks like classification and segmentation.
L. Scheibenreif +3 more
semanticscholar +1 more source
5G/B5G Service Classification Using Supervised Learning
Applied Sciences, 2021 The classification of services in 5G/B5G (Beyond 5G) networks has become important for telecommunications service providers, who face the challenge of simultaneously offering a better Quality of Service (QoS) in their networks and a better Quality of ...
Jorge E. Preciado-Velasco +4 more
doaj +1 more source
Classification under Streaming Emerging New Classes: A Solution using Completely Random Trees [PDF]
, 2016 This paper investigates an important problem in stream mining, i.e., classification under streaming emerging new classes or SENC. The common approach is to treat it as a classification problem and solve it using either a supervised learner or a semi ...
Mu, Xin, Ting, Kai Ming, Zhou, Zhi-Hua
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SSBTCNet: Semi-Supervised Brain Tumor Classification Network
IEEE Access, 2023 Classification of brain tumors from the Magnetic Resonance Imaging (MRI) is a vital and challenging task for brain tumor diagnosis. Despite favorable results, from current Deep Learning (DL) methods used for the classification of brain tumors, the ...
Zubair Atha, Jyotismita Chaki
doaj +1 more source
Self-Supervised EEG Emotion Recognition Models Based on CNN
IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023 Emotion plays crucial roles in human life. Recently, emotion classification from electroencephalogram (EEG) signal has attracted attention by researchers due to the rapid development of brain computer interface (BCI) techniques and machine learning ...
Xingyi Wang +5 more
doaj +1 more source
Learning Incoherent Subspaces: Classification via Incoherent Dictionary Learning [PDF]
, 2015 In this article we present the supervised iterative projections and rotations (s-ipr) algorithm, a method for learning discriminative incoherent subspaces from data.
D Barchiesi +8 more
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Generalized Maximum Entropy for Supervised Classification [PDF]
IEEE Transactions on Information Theory, 2020 The maximum entropy principle advocates to evaluate events’ probabilities using a distribution that maximizes entropy among those that satisfy certain expectations’ constraints.
S. Mazuelas, Yuan Shen, Aritz Pérez
semanticscholar +1 more source
Task-Driven Dictionary Learning [PDF]
, 2012 Modeling data with linear combinations of a few elements from a learned dictionary has been the focus of much recent research in machine learning, neuroscience and signal processing.
Bach, Francis +2 more
core +7 more sources