Results 141 to 150 of about 238,312 (311)

Semi-Supervised Machine Learning & Deep Learning Models in Crisis-Related Informativeness Classification

, 2019
This study examines the impact of several state-of-the-art Machine Learning and Deep Learning techniques in the context of semi-supervised disaster-related Twitter mining.
Alessandro Rennola (8973167)
core   +1 more source

Electric‐Current‐Induced Phase Transformation in Cu6Sn5 Below Its Equilibrium Transition Temperature

Advanced Science, EarlyView.
Electric current induces a monoclinic‐to‐hexagonal phase transformation in Cu6Sn5 at a measured bulk temperature of ∼120°C, below the equilibrium transition temperature. Ex situ synchrotron x‐ray diffraction, TEM, and matched thermal controls show that current stressing promotes the formation of a retained hexagonal η‐phase post‐stress state not ...
Shih‐kang Lin, Shubhayan Mukherjee, Yu‐chen Liu, Jun Mizuno   +3 more
wiley   +1 more source

A topological approach for semi-supervised learning

Journal of Computational Science
Nowadays, Machine Learning and Deep Learning methods have become the state-of-the-art approach to solve data classification tasks. In order to use those methods, it is necessary to acquire and label a considerable amount of data; however, this is not straightforward in some fields, since data annotation is time consuming and might require expert ...
Adrián Inés, César Domínguez 0001, Jónathan Heras, Gadea Mata, Julio Rubio 0001   +4 more
openaire   +2 more sources

A Framework for Context-Aware Semi Supervised Learning

, 2014
Supervised learning techniques require large number of labeled examples to build a classifier which is often difficult and expensive to collect. Unsupervised learning techniques, even though do not require labeled examples often form clusters regardless
Vijaya Geeta Dharmavaram, Shashi Mogalla
core  

STAID: A Self‐Refining Deep Learning Framework for Spatial Cell‐Type Deconvolution with Biologically Informed Modeling

Advanced Science, EarlyView.
STAID is a unified deep learning framework that couples iterative pseudo‐spot refinement with neural network training through a feedback loop and exploits gene co‐expression information to model higher‐order interactions, achieving accurate and robust cell‐type deconvolution in spatial transcriptomics.
Jixin Liu, Shuli Sun, Zhengliang Lv, Xinyu Liu, Yihua Wang, Bingqiang Liu   +5 more
wiley   +1 more source

Entropy‐guided contrastive learning for semi‐supervised medical image segmentation

IET Image Processing
Accurately segmenting medical images is a critical step in clinical diagnosis and developing patient‐specific treatment plans. While supervised learning algorithms have achieved excellent performance in this area, they require a large amount of annotated
Junsong Xie, Qian Wu, Renju Zhu
doaj   +1 more source

Reinforcement Learning Guided Semi-Supervised Learning

Advances in Neural Information Processing Systems 37
In recent years, semi-supervised learning (SSL) has gained significant attention due to its ability to leverage both labeled and unlabeled data to improve model performance, especially when labeled data is scarce. However, most current SSL methods rely on heuristics or predefined rules for generating pseudo-labels and leveraging unlabeled data.
Marzi Heidari, Hanping Zhang, Yuhong Guo
openaire   +3 more sources

Automatic annotation for weakly supervised learning of detectors

, 2012
PhDObject detection in images and action detection in videos are among the most widely studied computer vision problems, with applications in consumer photography, surveillance, and automatic media tagging. Typically, these standard detectors are fully
Siva, Parthipan
core  

Local semi-supervised regression for single-image super-resolution

, 2011
In this paper, we propose a local semi-supervised learning-based algorithm for single-image super-resolution. Different from most of example-based algorithms, the information of test patches is considered during learning local regression functions which ...
Xiaoli Pan, LiLuoqing, Luoqing Li, Li, Xuelong, Yan, P., Pingkun Yan, YuanYuan, Yuan Yuan, Yi Tang, TangYi, Yuan, Y., YanPingkun, Tang, Y., PanXiaoli, Pan, X., Xuelong Li, LiXuelong, Li, L.   +17 more
core   +1 more source

Noninvasive Characterization of Tumor Heterogeneity in HNSCC: From Clinical Utility to Biological Correlates

Advanced Science, EarlyView.
This study establishes a CT‐based radiomics framework to quantify intratumoral heterogeneity (ITH) in HNSCC. Using unsupervised clustering, tumor ROIs and VOIs are analyzed to calculate 2D/3D ITH scores. The score shows strong predictive value for prognosis and immunotherapy response, and is associated with tumor metabolism and immune microenvironment,
Xinwei Chen, Rui Liu, Yuan Zhou, Yin Xiao, Chengmin Feng, Lin Chen, Yanshi Li, Mengna Wang, Zhaobo Cheng, Yuxi Luo, Long Liu, Lijing Niu, Yue Xiang, Juan Peng, Guohua Hu, Min Pan   +15 more
wiley   +1 more source