Results 51 to 60 of about 338,614 (301)
Semi-Supervised Learning for Neural Machine Translation
, 2016 While end-to-end neural machine translation (NMT) has made remarkable progress recently, NMT systems only rely on parallel corpora for parameter estimation. Since parallel corpora are usually limited in quantity, quality, and coverage, especially for low-
Cheng, Yong +6 more
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Advanced Functional Materials, EarlyView.A 3D bone scaffold with osteogenic properties and capable of hardening in vivo is developed. The scaffold is implanted in a ductile state, and a phase transformation of the ceramic induces the stiffening and strengthening of the scaffold in vivo. Abstract Calcium phosphate 3D printing has revolutionized customized bone grafting.
Miguel Mateu‐Sanz +7 more
wiley +1 more source
A semi-supervised spam mail detector [PDF]
, 2006 This document describes a novel semi-supervised approach to spam classification, which was successful at the ECML/PKDD 2006 spam classification challenge.
Pfahringer, Bernhard
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Lγ-PageRank for semi-supervised learning [PDF]
Applied Network Science, 2019 PageRank for Semi-Supervised Learning has shown to leverage data structures and limited tagged examples to yield meaningful classification. Despite successes, classification performance can still be improved, particularly in cases of fuzzy graphs or unbalanced labeled data.
Esteban Bautista +2 more
openaire +2 more sources
Advanced Functional Materials, EarlyView.This review explores functional and responsive materials for triboelectric nanogenerators (TENGs) in sustainable smart agriculture. It examines how particulate contamination and dirt affect charge transfer and efficiency. Environmental challenges and strategies to enhance durability and responsiveness are outlined, including active functional layers ...
Rafael R. A. Silva +9 more
wiley +1 more source
SEMI-SUPERVISED MARGINAL FISHER ANALYSIS FOR HYPERSPECTRAL IMAGE CLASSIFICATION [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2012 The problem of learning with both labeled and unlabeled examples arises frequently in Hyperspectral image (HSI) classification. While marginal Fisher analysis is a supervised method, which cannot be directly applied for Semi-supervised classification ...
H. Huang, J. Liu, Y. Pan
doaj +1 more source
Geostatistical semi-supervised learning for spatial prediction
Artificial Intelligence in Geosciences, 2022 Geoscientists are increasingly tasked with spatially predicting a target variable in the presence of auxiliary information using supervised machine learning algorithms.
Francky Fouedjio, Hassan Talebi
doaj +1 more source
Semi-Supervised Learning for Neural Keyphrase Generation
, 2018 We study the problem of generating keyphrases that summarize the key points for a given document. While sequence-to-sequence (seq2seq) models have achieved remarkable performance on this task (Meng et al., 2017), model training often relies on large ...
Wang, Lu, Ye, Hai
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Advanced Functional Materials, EarlyView.This review highlights how machine learning (ML) algorithms are employed to enhance sensor performance, focusing on gas and physical sensors such as haptic and strain devices. By addressing current bottlenecks and enabling simultaneous improvement of multiple metrics, these approaches pave the way toward next‐generation, real‐world sensor applications.
Kichul Lee +17 more
wiley +1 more source
Graph Laplacian for Semi-supervised Learning
, 2023 Semi-supervised learning is highly useful in common scenarios where labeled data is scarce but unlabeled data is abundant. The graph (or nonlocal) Laplacian is a fundamental smoothing operator for solving various learning tasks. For unsupervised clustering, a spectral embedding is often used, based on graph-Laplacian eigenvectors.
Streicher, Or, Gilboa, Guy
openaire +2 more sources