Results 21 to 30 of about 235,048 (260)
Muffled Semi-Supervised Learning
CoRR, 2016 We explore a novel approach to semi-supervised learning. This approach is contrary to the common approach in that the unlabeled examples serve to "muffle," rather than enhance, the guidance provided by the labeled examples. We provide several variants of the basic algorithm and show experimentally that they can achieve significantly higher AUC than ...
Akshay Balsubramani, Yoav Freund
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Quantum semi-supervised kernel learning
Quantum Machine Intelligence, 2021 Quantum computing leverages quantum effects to build algorithms that are faster then their classical variants. In machine learning, for a given model architecture, the speed of training the model is typically determined by the size of the training dataset.
Seyran Saeedi +2 more
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Tracking-based semi-supervised learning [PDF]
The International Journal of Robotics Research, 2011 We consider a semi-supervised approach to the problem of track classification in dense three-dimensional range data. This problem involves the classification of objects that have been segmented and tracked without the use of a class-specific tracker. This paper is an extended version of our previous work.
Alex Teichman, Sebastian Thrun
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Semi-supervised Sequence Learning
CoRR, 2015 We present two approaches that use unlabeled data to improve sequence learning with recurrent networks. The first approach is to predict what comes next in a sequence, which is a conventional language model in natural language processing. The second approach is to use a sequence autoencoder, which reads the input sequence into a vector and predicts the
Andrew M. Dai, Quoc V. Le
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Feature ranking for semi-supervised learning
Machine Learning, 2022 AbstractThe data used for analysis are becoming increasingly complex along several directions: high dimensionality, number of examples and availability of labels for the examples. This poses a variety of challenges for the existing machine learning methods, related to analyzing datasets with a large number of examples that are described in a high ...
Matej Petkovic +2 more
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Semi-supervised learning with regularized Laplacian [PDF]
Optimization Methods and Software, 2016 We study a semi-supervised learning method based on the similarity graph and RegularizedLaplacian. We give convenient optimization formulation of the Regularized Laplacian method and establishits various properties. In particular, we show that the kernel of the methodcan be interpreted in terms of discrete and continuous time random walks and possesses
Konstantin Avrachenkov +2 more
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A Semi-Supervised Learning Method for Vietnamese Part of Speech Tagging [PDF]
, 2010 This paper presents a semi-supervised learning method for Vietnamese part of speech tagging. We take into account two powerful tagging models including Conditional Random Fields (CRFs)and the Guided Online-Learning models (GLs) as base learning models ...
Nguyen, Viet Cuong +4 more
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Cyclic label propagation for graph semi-supervised learning
, 2021 Graph neural networks (GNNs) have emerged as effective approaches for graph analysis, especially in the scenario of semi-supervised learning. Despite its success, GNN often suffers from over-smoothing and over-fitting problems, which affects its ...
Bu, Jiajun +5 more
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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
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Semi-Supervised Learning with Scarce Annotations [PDF]
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2020 Workshop on Deep Vision, CVPR ...
Rebuffi, S-A +4 more
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