Results 51 to 60 of about 137,057 (303)

Deep Temporal Iterative Clustering for Satellite Image Time Series Land Cover Analysis

Remote Sensing, 2022
The extensive amount of Satellite Image Time Series (SITS) data brings new opportunities and challenges for land cover analysis. Many supervised machine learning methods have been applied in SITS, but the labeled SITS samples are time- and effort ...
Wenqi Guo, Weixiong Zhang, Zheng Zhang, Ping Tang, Shichen Gao   +4 more
doaj   +1 more source

Supervising Unsupervised Learning

CoRR, 2017
We introduce a framework to leverage knowledge acquired from a repository of (heterogeneous) supervised datasets to new unsupervised datasets. Our perspective avoids the subjectivity inherent in unsupervised learning by reducing it to supervised learning, and provides a principled way to evaluate unsupervised algorithms.
Vikas K. Garg 0001, Adam Kalai
openaire   +2 more sources

A Brief Review of Unsupervised Machine Learning Algorithms in Astronomy: Dimensionality Reduction and Clustering

Universe
This review investigates the application of unsupervised machine learning algorithms to astronomical data. Unsupervised machine learning enables researchers to analyze large, high-dimensional, and unlabeled datasets and is sometimes considered more ...
Chih-Ting Kuo, Duo Xu, Rachel Friesen
doaj   +1 more source

Unsupervised Learning of Monocular Depth and Ego-Motion with Optical Flow Features and Multiple Constraints

Sensors, 2022
This paper proposes a novel unsupervised learning framework for depth recovery and camera ego-motion estimation from monocular video. The framework exploits the optical flow (OF) property to jointly train the depth and the ego-motion models.
Baigan Zhao, Yingping Huang, Wenyan Ci, Xing Hu   +3 more
doaj   +1 more source

Prefix Data Augmentation for Contrastive Learning of Unsupervised Sentence Embedding

Applied Sciences
This paper presents prefix data augmentation (Prd) as an innovative method for enhancing sentence embedding learning through unsupervised contrastive learning.
Chunchun Wang, Shu Lv
doaj   +1 more source

Developmental programmes drive cellular plasticity, disease progression and therapy resistance in lung adenocarcinoma

Molecular Oncology, EarlyView.
This study shows that lung adenocarcinomas exploit developmental branching morphogenesis to acquire a therapy resistant basal‐like tumour cell state. This process was found to be regulated by combined TP53 loss‐of‐function and type‐I interferon signalling, identifying a novel axis for biomarker and therapeutic target discovery.
Kamila J Bienkowska, Stephany Gallardo, Nur S. Zainal, Leena Arora, Matthew Ellis, Maria‐Antoinette Lopez, Judith Austine, Sai Pittla, Serena J Chee, Aiman Alzetani, Emily C Shaw, Christian H Ottensmeier, Gareth J Thomas, Christopher J Hanley   +13 more
wiley   +1 more source

Bayesian Ying-Yang System and Theory as a Unified Statistical Learning Approach (I): for Unsupervised and Semi-Unsupervised Learning [PDF]

, 1997
. A unified statistical learning approach called Bayesian YingYang (BYY) system and theory has been developed by the present author in recent years. This paper is the first part of a recent effort on systematically summarizing this theory. In this paper,
Semi-unsupervised Learning, Lei Xu, For Unsupervised, S. Amari, N. Kassabov, An Invited Book Chapter   +5 more
core  

Single‐cell DNA methylation profiling: Technologies, computation, and applications in precision oncology

Molecular Oncology, EarlyView.
Single‐cell DNA methylation (scDNAme) profiling maps epimutational clonal evolution, revealing mechanisms of malignancy and therapeutic resistance across diverse cancer types. By providing a high‐resolution landscape of intratumoral heterogeneity, these technologies empower precise patient stratification, guide the development of enhanced ...
Ik Soo Kim
wiley   +1 more source

Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning [PDF]

, 2014
Automatic species classification of birds from their sound is a computational tool of increasing importance in ecology, conservation monitoring and vocal communication studies.
Plumbley, Mark D., Stowell, D, Plumbley, MD, Dan Stowell, Mark D. Plumbley, Stowell, Dan; id_orcid, Stowell, Dan   +6 more
core   +1 more source

Unsupervised Algorithms to Detect Zero-Day Attacks: Strategy and Application

IEEE Access, 2021
In the last decade, researchers, practitioners and companies struggled for devising mechanisms to detect cyber-security threats. Among others, those efforts originated rule-based, signature-based or supervised Machine Learning (ML) algorithms that were ...
Tommaso Zoppi, Andrea Ceccarelli, Andrea Bondavalli   +2 more
doaj   +1 more source