The Experts below are selected from a list of 19512 Experts worldwide ranked by ideXlab platform
Takafumi Kanamori - One of the best experts on this subject based on the ideXlab platform.
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Semi-supervised learning with density-ratio estimation
Machine Learning, 2013Co-Authors: Masanori Kawakita, Takafumi KanamoriAbstract:In this paper we study statistical properties of semi-supervised learning, which is considered to be an important problem in the field of machine learning. In standard supervised learning only labeled data is observed, and classification and regression problems are formalized as supervised learning. On the other hand, in semi-supervised learning, unlabeled data is also obtained in addition to labeled data. Hence, the ability to exploit unlabeled data is important to improve prediction accuracy in semi-supervised learning. This problem is regarded as a Semiparametric estimation problem with missing data. Under discriminative probabilistic Models, it was considered that unlabeled data is useless to improve the estimation accuracy. Recently, the weighted estimator using unlabeled data achieves a better prediction accuracy compared to the learning method using only labeled data, especially when the discriminative probabilistic Model is misspecified. That is, improvement under the Semiparametric Model with missing data is possible when the Semiparametric Model is misspecified. In this paper, we apply the density-ratio estimator to obtain the weight function in semi-supervised learning. Our approach is advantageous because the proposed estimator does not require well-specified probabilistic Models for the probability of the unlabeled data. Based on statistical asymptotic theory, we prove that the estimation accuracy of our method outperforms supervised learning using only labeled data. Some numerical experiments present the usefulness of our methods.
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Semi-Supervised learning with Density-Ratio Estimation
arXiv: Machine Learning, 2012Co-Authors: Masanori Kawakita, Takafumi KanamoriAbstract:In this paper, we study statistical properties of semi-supervised learning, which is considered as an important problem in the community of machine learning. In the standard supervised learning, only the labeled data is observed. The classification and regression problems are formalized as the supervised learning. In semi-supervised learning, unlabeled data is also obtained in addition to labeled data. Hence, exploiting unlabeled data is important to improve the prediction accuracy in semi-supervised learning. This problems is regarded as a Semiparametric estimation problem with missing data. Under the the discriminative probabilistic Models, it had been considered that the unlabeled data is useless to improve the estimation accuracy. Recently, it was revealed that the weighted estimator using the unlabeled data achieves better prediction accuracy in comparison to the learning method using only labeled data, especially when the discriminative probabilistic Model is misspecified. That is, the improvement under the Semiparametric Model with missing data is possible, when the Semiparametric Model is misspecified. In this paper, we apply the density-ratio estimator to obtain the weight function in the semi-supervised learning. The benefit of our approach is that the proposed estimator does not require well-specified probabilistic Models for the probability of the unlabeled data. Based on the statistical asymptotic theory, we prove that the estimation accuracy of our method outperforms the supervised learning using only labeled data. Some numerical experiments present the usefulness of our methods.
Masanori Kawakita - One of the best experts on this subject based on the ideXlab platform.
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Semi-supervised learning with density-ratio estimation
Machine Learning, 2013Co-Authors: Masanori Kawakita, Takafumi KanamoriAbstract:In this paper we study statistical properties of semi-supervised learning, which is considered to be an important problem in the field of machine learning. In standard supervised learning only labeled data is observed, and classification and regression problems are formalized as supervised learning. On the other hand, in semi-supervised learning, unlabeled data is also obtained in addition to labeled data. Hence, the ability to exploit unlabeled data is important to improve prediction accuracy in semi-supervised learning. This problem is regarded as a Semiparametric estimation problem with missing data. Under discriminative probabilistic Models, it was considered that unlabeled data is useless to improve the estimation accuracy. Recently, the weighted estimator using unlabeled data achieves a better prediction accuracy compared to the learning method using only labeled data, especially when the discriminative probabilistic Model is misspecified. That is, improvement under the Semiparametric Model with missing data is possible when the Semiparametric Model is misspecified. In this paper, we apply the density-ratio estimator to obtain the weight function in semi-supervised learning. Our approach is advantageous because the proposed estimator does not require well-specified probabilistic Models for the probability of the unlabeled data. Based on statistical asymptotic theory, we prove that the estimation accuracy of our method outperforms supervised learning using only labeled data. Some numerical experiments present the usefulness of our methods.
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Semi-Supervised learning with Density-Ratio Estimation
arXiv: Machine Learning, 2012Co-Authors: Masanori Kawakita, Takafumi KanamoriAbstract:In this paper, we study statistical properties of semi-supervised learning, which is considered as an important problem in the community of machine learning. In the standard supervised learning, only the labeled data is observed. The classification and regression problems are formalized as the supervised learning. In semi-supervised learning, unlabeled data is also obtained in addition to labeled data. Hence, exploiting unlabeled data is important to improve the prediction accuracy in semi-supervised learning. This problems is regarded as a Semiparametric estimation problem with missing data. Under the the discriminative probabilistic Models, it had been considered that the unlabeled data is useless to improve the estimation accuracy. Recently, it was revealed that the weighted estimator using the unlabeled data achieves better prediction accuracy in comparison to the learning method using only labeled data, especially when the discriminative probabilistic Model is misspecified. That is, the improvement under the Semiparametric Model with missing data is possible, when the Semiparametric Model is misspecified. In this paper, we apply the density-ratio estimator to obtain the weight function in the semi-supervised learning. The benefit of our approach is that the proposed estimator does not require well-specified probabilistic Models for the probability of the unlabeled data. Based on the statistical asymptotic theory, we prove that the estimation accuracy of our method outperforms the supervised learning using only labeled data. Some numerical experiments present the usefulness of our methods.
Paweł Krajewski - One of the best experts on this subject based on the ideXlab platform.
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Erratum to “Constrained estimators of treatment parameters in Semiparametric Models” [Statist. Probab. Lett. 77 (2007) 914–919]
Statistics & Probability Letters, 2008Co-Authors: Marcin Przystalski, Paweł KrajewskiAbstract:Abstract In [Przystalski M., Krajewski P., 2007. Constrained estimators of treatment parameters in Semiparametric Models. Statist. Probab. Lett. 77, 914–919] we presented a method of estimation of treatment effects in a Semiparametric Model with one smoothing term under additional conditions on their linear functions and its application to hypothesis testing. In this note we will present the corrected version of the proof of Theorem 3.1.
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Constrained estimators of treatment parameters in Semiparametric Models
Statistics & Probability Letters, 2007Co-Authors: Marcin Przystalski, Paweł KrajewskiAbstract:Semiparametric Models are generalizations of parametric regression Models. We present a method of estimation of treatment effects in a Semiparametric Model with one smoothing term under additional conditions on their linear functions and its application to hypothesis testing.
Alfred O Hero - One of the best experts on this subject based on the ideXlab platform.
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nonlinear unmixing of hyperspectral images using a Semiparametric Model and spatial regularization
International Conference on Acoustics Speech and Signal Processing, 2014Co-Authors: Jie Chen, Cedric Richard, Alfred O HeroAbstract:Incorporating spatial information into hyperspectral unmixing procedures has been shown to have positive effects, due to the inherent spatial-spectral duality in hyperspectral scenes. Current research works that consider spatial information are mainly focused on the linear mixing Model. In this paper, we investigate a variational approach to incorporating spatial correlation into a nonlinear unmixing procedure. A nonlinear algorithm operating in reproducing kernel Hilbert spaces, associated with an `1 local variation norm as the spatial regularizer, is derived. Experimental results, with both synthetic and real data, illustrate the effectiveness of the proposed scheme.
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Nonlinear unmixing of hyperspectral images using a Semiparametric Model and spatial regularization
arXiv: Machine Learning, 2013Co-Authors: Jie Chen, Cedric Richard, Alfred O HeroAbstract:Incorporating spatial information into hyperspectral unmixing procedures has been shown to have positive effects, due to the inherent spatial-spectral duality in hyperspectral scenes. Current research works that consider spatial information are mainly focused on the linear mixing Model. In this paper, we investigate a variational approach to incorporating spatial correlation into a nonlinear unmixing procedure. A nonlinear algorithm operating in reproducing kernel Hilbert spaces, associated with an $\ell_1$ local variation norm as the spatial regularizer, is derived. Experimental results, with both synthetic and real data, illustrate the effectiveness of the proposed scheme.
Paul S. F. Yip - One of the best experts on this subject based on the ideXlab platform.
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A Semiparametric Model for Recapture Experiments
Scandinavian Journal of Statistics, 2003Co-Authors: Yan Wang, Paul S. F. YipAbstract:A two-step procedure based on the conditional likelihood is proposed to estimate the population size of a closed population using a Semiparametric Model for recapture studies. An asymptotic variance estimate and numerical results are presented. The method is applied to a bird banding dataset in Hong Kong.link_to_subscribed_fulltex
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A Semiparametric Model for Recapture Experiments
Scandinavian Journal of Statistics, 2003Co-Authors: Yan Wang, Paul S. F. YipAbstract:A two-step procedure based on the conditional likelihood is proposed to estimate the population size of a closed population using a Semiparametric Model for recapture studies. An asymptotic variance estimate and numerical results are presented. The method is applied to a bird banding dataset in Hong Kong.
