Zobrazeno 1 - 4
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pro vyhledávání: '"Jiwoong Im"'
Autor:
Graham W. Taylor, Daniel Jiwoong Im
Publikováno v:
IEEE Geoscience and Remote Sensing Letters. 12:1913-1917
In problems where labeled data are scarce, semisupervised learning (SSL) techniques are an attractive framework that can exploit both labeled and unlabeled data. These approaches typically rely on a smoothness assumption such that examples that are s
Autor:
Daniel Jiwoong Im, Graham W. Taylor
Publikováno v:
IJCNN
Naive Bayes Nearest Neighbour (NBNN) is a simple and effective framework which addresses many of the pitfalls of K-Nearest Neighbour (KNN) classification. It has yielded competitive results on several computer vision benchmarks. Its central tenet is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::dcc4aa8bf7e80beda8523c2c05b97f26
http://arxiv.org/abs/1607.03050
http://arxiv.org/abs/1607.03050
Publikováno v:
Machine Learning and Knowledge Discovery in Databases ISBN: 9783319235271
ECML/PKDD (1)
ECML/PKDD (1)
Energy-based models are popular in machine learning due to the elegance of their formulation and their relationship to statistical physics. Among these, the Restricted Boltzmann Machine (RBM), and its staple training algorithm contrastive divergence
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::58f0eddb31fb51ae4a836a868f8bea0c
https://doi.org/10.1007/978-3-319-23528-8_30
https://doi.org/10.1007/978-3-319-23528-8_30
Autor:
Daniel Jiwoong Im, Graham W. Taylor
Publikováno v:
Machine Learning and Knowledge Discovery in Databases ISBN: 9783319235271
ECML/PKDD (1)
ECML/PKDD (1)
Auto-encoders are perhaps the best-known non-probabilistic methods for representation learning. They are conceptually simple and easy to train. Recent theoretical work has shed light on their ability to capture manifold structure, and drawn connectio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bc5115e45dc3c53004540d889e221b69
https://doi.org/10.1007/978-3-319-23528-8_33
https://doi.org/10.1007/978-3-319-23528-8_33