Results 1 to 10 of about 235,048 (260)
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
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Semi-Supervised Active Learning in a stream-based scenario.
, 2016 Semi-Supervised Active Learning in a stream-based scenario.
Haifeng Li (142063) +6 more
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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
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
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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)
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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
, 2005 We apply classic online learning techniques similar to the perceptron algorithm to the problem of learning a function defined on a graph. The benefit of our approach includes simple algorithms and performance guarantees that we naturally interpret in ...
Mark Herbster +5 more
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Learning from Partial Labels with Minimum Entropy [PDF]
This paper introduces the minimum entropy regularizer for learning from partial labels. This learning problem encompasses the semi-supervised setting, where a decision rule is to be learned from labeled and unlabeled examples.
Yoshua Bengio, Yves Grandvalet
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Semi-supervised learning : from Gaussian fields to Gaussian processes
, 2018 : "We show that the Gaussian random fields and harmonic energy minimizing function framework for semi-supervised learning can be viewed in terms of Gaussian processes, with covariance matrices derived from the graph Laplacian.
Zoubin Ghahramani (5363936) +2 more
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