Results 111 to 120 of about 238,312 (311)
Cross‐Scale Hierarchical Targeted Delivery System Based on Small‐Scale Magnetic Robots
Advanced Robotics Research, EarlyView.This article reviews a cross‐scale hierarchical targeted delivery system that integrates magnetic continuum robots and magnetic microrobots. By combining rapid long‐range navigation with precise microscale targeting, the system overcomes key limitations of single‐scale approaches.
Junjian Zhou +4 more
wiley +1 more source
Semi-supervised Learning for WLAN Positioning [PDF]
, 2011 Currently the most accurate WLAN positioning systems are based on the fingerprinting approach, where a "radio map" is constructed by modeling how the signal strength measurements vary according to the location. However, collecting a sufficient amount of location-tagged training data is a rather tedious and time consuming task, especially in indoor ...
Teemu Pulkkinen +2 more
openaire +1 more source
Efficient Non-Parametric Function Induction in Semi-Supervised Learning [PDF]
There has been an increase of interest for semi-supervised learning recently, because of the many datasets with large amounts of unlabeled examples and only a few labeled ones.
Yoshua Bengio +2 more
core
Semi-Supervised Active Learning in a stream-based scenario.
, 2016 Semi-Supervised Active Learning in a stream-based scenario.
Haifeng Li (142063) +6 more
core +1 more source
Advanced Robotics Research, EarlyView.This review maps the methods to monitor robots’ health by fusing vibration, sound, control signals, vision, force, and oil information with artificial intelligence. It identifies deep learning, transfer learning, digital twins, and physics‐informed models as key methodological pathways enabling earlier diagnosis, safer human–robot collaboration, and ...
Yuting Qiao +6 more
wiley +1 more source
Multiview Semi-Supervised Learning with Consensus
IEEE Transactions on Knowledge and Data Engineering, 2012 Obtaining high-quality and up-to-date labeled data can be difficult in many real-world machine learning applications. Semi-supervised learning aims to improve the performance of a classifier trained with limited number of labeled data by utilizing the unlabeled ones.
Guangxia Li +2 more
openaire +4 more sources
A Taxonomy for Semi-Supervised Learning Methods [PDF]
We propose a simple taxonomy of probabilistic graphical models for the semi-supervised learning problem. We give some broad classes of algorithms for each of the families and point to specific realizations in the literature.
Seeger, Matthias
core
Semi-Supervised Active Learning in a pool-based scenario.
, 2016 Semi-Supervised Active Learning in a pool-based scenario.
Haifeng Li (142063) +6 more
core +1 more source
Semi-Supervised Learning Approach for Bladder Cancer Diagnosis
, 2023 Recent studies have made great strides in reducing the labeling burden in deep learning algorithms by requiring that only a subset of the dataset be labeled. These are called semi-supervised learning algorithms (SSL).
Kenneth Wenger (16892040)
core +1 more source
Advanced Science, EarlyView.A time‐multiplexed nanobody‐functionalized organic electrochemical transistor enables rapid and sensitive detection of three respiratory viral proteins from saliva. Sequentially addressed multi‐gate electrodes on a shared channel achieve around 1 fm detection within 15 min without sample preprocessing. Clinical validation demonstrates high specificity,
Tianrui Chang +13 more
wiley +1 more source