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Results 1 - 10
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119,046
On Manifold Regularization
, 2005
"... We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semisupervised framework that incorporates labeled and unlabeled data in a generalpurpose learner. Some transductive graph learni ..."
Abstract
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Cited by 96 (0 self)
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We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semisupervised framework that incorporates labeled and unlabeled data in a generalpurpose learner. Some transductive graph
Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
- JOURNAL OF MACHINE LEARNING RESEARCH
, 2006
"... We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semi-supervised framework that incorporates labeled and unlabeled data in a general-purpose learner. Some transductive graph learning al ..."
Abstract
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Cited by 560 (15 self)
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We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semi-supervised framework that incorporates labeled and unlabeled data in a general-purpose learner. Some transductive graph learning
Vector-valued Manifold Regularization
"... We consider the general problem of learning an unknown functional dependency, f: X ↦ → Y, between a structured input space X and a structured output space Y, fromlabeled and unlabeled examples. We formulate this problem in terms of data-dependent regularization in Vector-valued Reproducing Kernel Hi ..."
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Cited by 7 (4 self)
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Hilbert Spaces (Micchelli & Pontil, 2005) whichelegantlyextendfamiliarscalarvalued kernel methods to the general setting where Y has a Hilbert space structure. Our methods provide a natural extension of Manifold Regularization (Belkin et al., 2006) algorithms to also exploit output inter
Large-scale sparsified manifold regularization
- Advances in Neural Information Processing Systems (NIPS) 19
, 2006
"... Semi-supervised learning is more powerful than supervised learning by using both labeled and unlabeled data. In particular, the manifold regularization framework, together with kernel methods, leads to the Laplacian SVM (LapSVM) that has demonstrated state-of-the-art performance. However, the LapSVM ..."
Abstract
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Cited by 23 (3 self)
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Semi-supervised learning is more powerful than supervised learning by using both labeled and unlabeled data. In particular, the manifold regularization framework, together with kernel methods, leads to the Laplacian SVM (LapSVM) that has demonstrated state-of-the-art performance. However, the Lap
On Manifold Regularization Mikhail Belkin
, 2004
"... We propose a family of learning algorithms based on a new form of regularization which allows us to incorporate both labeled and unlabeled data in a general-purpose learner. Transductive graph learning algorithms and standard methods including SVM and Regularized Least Squares can be obtained as spe ..."
Abstract
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We propose a family of learning algorithms based on a new form of regularization which allows us to incorporate both labeled and unlabeled data in a general-purpose learner. Transductive graph learning algorithms and standard methods including SVM and Regularized Least Squares can be obtained
Manifold Regularization for SIR with Rate Root-n Convergence
"... In this paper, we study the manifold regularization for the Sliced Inverse Regression (SIR). The manifold regularization improves the standard SIR in two aspects: 1) it encodes the local geometry for SIR and 2) it enables SIR to deal with transductive and semi-supervised learning problems. We prove ..."
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Cited by 3 (0 self)
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In this paper, we study the manifold regularization for the Sliced Inverse Regression (SIR). The manifold regularization improves the standard SIR in two aspects: 1) it encodes the local geometry for SIR and 2) it enables SIR to deal with transductive and semi-supervised learning problems. We prove
Manifold Regularization for Structured Outputs via the Joint Kernel
"... Abstract — By utilizing the label dependencies among both the labeled and unlabeled data, semi-supervised learning often has better generalization performance than supervised learning. In this paper, we extend a popular graph-based semi-supervised learning method, namely, manifold regularization, to ..."
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Abstract — By utilizing the label dependencies among both the labeled and unlabeled data, semi-supervised learning often has better generalization performance than supervised learning. In this paper, we extend a popular graph-based semi-supervised learning method, namely, manifold regularization
Solution Path for Manifold Regularized Semisupervised Classification
"... Abstract—Traditional learning algorithms use only labeled data for training. However, labeled examples are often difficult or time consuming to obtain since they require substantial human labeling efforts. On the other hand, unlabeled data are often relatively easy to collect. Semisupervised learnin ..."
Abstract
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Cited by 3 (0 self)
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learning addresses this problem by using large quantities of unlabeled data with labeled data to build better learning algorithms. In this paper, we use the manifold regularization approach to formulate the semisupervised learning problem where a regularization framework which balances a tradeoff between
Laplacian Embedded Regression for Scalable Manifold Regularization
"... Semi-supervised Learning (SSL), as a powerful tool to learn from a limited number of labeled data and a large number of unlabeled data, has been attracting increasing attention in the machine learning community. In particular, the manifold regularization framework has laid solid theoretical foundat ..."
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Cited by 5 (0 self)
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Semi-supervised Learning (SSL), as a powerful tool to learn from a limited number of labeled data and a large number of unlabeled data, has been attracting increasing attention in the machine learning community. In particular, the manifold regularization framework has laid solid theoretical
Manifold regularization and semi-supervised learning: some theoretical analysis
, 2008
"... Manifold regularization (Belkin et al., 2006) is a geometrically motivated framework for machine learning within which several semi-supervised algorithms have been constructed. Here we try to provide some theoretical understanding of this approach. Our main result is to expose the natural structure ..."
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Cited by 19 (0 self)
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Manifold regularization (Belkin et al., 2006) is a geometrically motivated framework for machine learning within which several semi-supervised algorithms have been constructed. Here we try to provide some theoretical understanding of this approach. Our main result is to expose the natural structure
Results 1 - 10
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119,046
