The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Estimar a razão de duas funções de densidade de probabilidade (também conhecida como importância) recentemente atraiu muita atenção, uma vez que estimadores de importância podem ser usados para resolver vários problemas de aprendizado de máquina e mineração de dados. Neste artigo, propomos um novo método de estimativa de importância usando um mistura de analisadores probabilísticos de componentes principais. O método proposto é mais flexível do que as abordagens existentes e espera-se que funcione bem quando a função de importância alvo estiver correlacionada e com classificação deficiente. Através de experimentos, ilustramos a validade da abordagem proposta.
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Makoto YAMADA, Masashi SUGIYAMA, Gordon WICHERN, Jaak SIMM, "Direct Importance Estimation with a Mixture of Probabilistic Principal Component Analyzers" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 10, pp. 2846-2849, October 2010, doi: 10.1587/transinf.E93.D.2846.
Abstract: Estimating the ratio of two probability density functions (a.k.a. the importance) has recently gathered a great deal of attention since importance estimators can be used for solving various machine learning and data mining problems. In this paper, we propose a new importance estimation method using a mixture of probabilistic principal component analyzers. The proposed method is more flexible than existing approaches, and is expected to work well when the target importance function is correlated and rank-deficient. Through experiments, we illustrate the validity of the proposed approach.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.2846/_p
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@ARTICLE{e93-d_10_2846,
author={Makoto YAMADA, Masashi SUGIYAMA, Gordon WICHERN, Jaak SIMM, },
journal={IEICE TRANSACTIONS on Information},
title={Direct Importance Estimation with a Mixture of Probabilistic Principal Component Analyzers},
year={2010},
volume={E93-D},
number={10},
pages={2846-2849},
abstract={Estimating the ratio of two probability density functions (a.k.a. the importance) has recently gathered a great deal of attention since importance estimators can be used for solving various machine learning and data mining problems. In this paper, we propose a new importance estimation method using a mixture of probabilistic principal component analyzers. The proposed method is more flexible than existing approaches, and is expected to work well when the target importance function is correlated and rank-deficient. Through experiments, we illustrate the validity of the proposed approach.},
keywords={},
doi={10.1587/transinf.E93.D.2846},
ISSN={1745-1361},
month={October},}
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TY - JOUR
TI - Direct Importance Estimation with a Mixture of Probabilistic Principal Component Analyzers
T2 - IEICE TRANSACTIONS on Information
SP - 2846
EP - 2849
AU - Makoto YAMADA
AU - Masashi SUGIYAMA
AU - Gordon WICHERN
AU - Jaak SIMM
PY - 2010
DO - 10.1587/transinf.E93.D.2846
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2010
AB - Estimating the ratio of two probability density functions (a.k.a. the importance) has recently gathered a great deal of attention since importance estimators can be used for solving various machine learning and data mining problems. In this paper, we propose a new importance estimation method using a mixture of probabilistic principal component analyzers. The proposed method is more flexible than existing approaches, and is expected to work well when the target importance function is correlated and rank-deficient. Through experiments, we illustrate the validity of the proposed approach.
ER -