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On Computational Issues of Semi-Supervised Local Fisher Discriminant AnalysisSUGIYAMA, Masashi.IEICE transactions on information and systems. 2009, Vol 92, Num 5, pp 1204-1208, issn 0916-8532, 5 p.Article

Early Stopping Heuristics in Pool-Based Incremental Active Learning for Least-Squares Probabilistic ClassifierKOBAYASHI, Tsubasa; SUGIYAMA, Masashi.IEICE transactions on information and systems. 2012, Vol 95, Num 8, pp 2065-2073, issn 0916-8532, 9 p.Article

Least-Squares Independent Component AnalysisSUZUKI, Taiji; SUGIYAMA, Masashi.Neural computation. 2011, Vol 23, Num 1, pp 284-301, issn 0899-7667, 18 p.Article

A batch ensemble approach to active learning with model selectionSUGIYAMA, Masashi; RUBENS, Neil.Neural networks. 2008, Vol 21, Num 9, pp 1278-1286, issn 0893-6080, 9 p.Article

Winning the Kaggle Algorithmic Trading Challenge with the Composition of Many Models and Feature EngineeringABRIL, Ildefons Magrans; SUGIYAMA, Masashi.IEICE transactions on information and systems. 2013, Vol 96, Num 3, pp 742-745, issn 0916-8532, 4 p.Article

Canonical dependency analysis based on squared-loss mutual informationKARASUYAMA, Masayuki; SUGIYAMA, Masashi.Neural networks. 2012, Vol 34, pp 46-55, issn 0893-6080, 10 p.Article

Pool-based active learning in approximate linear regressionSUGIYAMA, Masashi; NAKAJIMA, Shinichi.Machine learning. 2009, Vol 75, Num 3, pp 249-274, issn 0885-6125, 26 p.Article

A new meta-criterion for regularized subspace information criterionHIDAKA, Yasushi; SUGIYAMA, Masashi.IEICE transactions on information and systems. 2007, Vol 90, Num 11, pp 1779-1786, issn 0916-8532, 8 p.Article

Constructing kernel functions for binary regressionSUGIYAMA, Masashi; OGAWA, Hidemitsu.IEICE transactions on information and systems. 2006, Vol 89, Num 7, pp 2243-2249, issn 0916-8532, 7 p.Article

On Kernel Parameter Selection in Hilbert-Schmidt Independence CriterionSUGIYAMA, Masashi; YAMADA, Makoto.IEICE transactions on information and systems. 2012, Vol 95, Num 10, pp 2564-2567, issn 0916-8532, 4 p.Article

The Degrees of Freedom of Partial Least Squares RegressionKRÄMER, Nicole; SUGIYAMA, Masashi.Journal of the American Statistical Association. 2011, Vol 106, Num 494, pp 697-705, issn 0162-1459, 9 p.Article

A unified method for optimizing linear image restoration filtersSUGIYAMA, Masashi; OGAWA, Hidemitsu.Signal processing. 2002, Vol 82, Num 11, pp 1773-1787, issn 0165-1684Article

Direct Approximation of Quadratic Mutual Information and Its Application to Dependence-Maximization ClusteringSAINUI, Janya; SUGIYAMA, Masashi.IEICE transactions on information and systems. 2013, Vol 96, Num 10, pp 2282-2285, issn 0916-8532, 4 p.Article

Least-Squares Independence TestSUGIYAMA, Masashi; SUZUKI, Taiji.IEICE transactions on information and systems. 2011, Vol 94, Num 6, pp 1333-1336, issn 0916-8532, 4 p.Article

Incremental active learning for optimal generalizationSUGIYAMA, Masashi; OGAWA, Hidemitsu.Neural computation. 2000, Vol 12, Num 12, pp 2909-2940, issn 0899-7667Article

Improving importance estimation in pool-based batch active learning for approximate linear regressionKURIHARA, Nozomi; SUGIYAMA, Masashi.Neural networks. 2012, Vol 36, pp 73-82, issn 0893-6080, 10 p.Article

Direct Importance Estimation with Gaussian Mixture ModelsYAMADA, Makoto; SUGIYAMA, Masashi.IEICE transactions on information and systems. 2009, Vol 92, Num 10, pp 2159-2162, issn 0916-8532, 4 p.Article

Optimal design of regularization term and regularization parameter by subspace information criterionSUGIYAMA, Masashi; OGAWA, Hidemitsu.Neural networks. 2002, Vol 15, Num 3, pp 349-361, issn 0893-6080Article

Model selection under covariate shiftSUGIYAMA, Masashi; MÜLLER, Klaus-Robert.Lecture notes in computer science. 2005, pp 235-240, issn 0302-9743, isbn 3-540-28755-8, 6 p.Conference Paper

Semi-supervised learning of class balance under class-prior change by distribution matchingDU PLESSIS, Marthinus Christoffel; SUGIYAMA, Masashi.Neural networks. 2014, Vol 50, pp 110-119, issn 0893-6080, 10 p.Article

Statistical analysis of kernel-based least-squares density-ratio estimationKANAMORI, Takafumi; SUZUKI, Taiji; SUGIYAMA, Masashi et al.Machine learning. 2012, Vol 86, Num 3, pp 335-367, issn 0885-6125, 33 p.Article

f-Divergence Estimation and Two-Sample Homogeneity Test Under Semiparametric Density-Ratio ModelsKANAMORI, Takafumi; SUZUKI, Taiji; SUGIYAMA, Masashi et al.IEEE transactions on information theory. 2012, Vol 58, Num 2, pp 708-720, issn 0018-9448, 13 p.Article

Dimensionality reduction for density ratio estimation in high-dimensional spacesSUGIYAMA, Masashi; KAWANABE, Motoaki; PUI LING CHUI et al.Neural networks. 2010, Vol 23, Num 1, pp 44-59, issn 0893-6080, 16 p.Article

Recent Advances and Trends in Large-Scale Kernel Methods : Large scale algorithms for learning and optimizationKASHIMA, Hisashi; IDE, Tsuyoshi; KATO, Tsuyoshi et al.IEICE transactions on information and systems. 2009, Vol 92, Num 7, pp 1338-1353, issn 0916-8532, 16 p.Article

Computationally Efficient Multi-Label Classification by Least-Squares Probabilistic ClassifiersNAM, Hyunha; HACHIYA, Hirotaka; SUGIYAMA, Masashi et al.IEICE transactions on information and systems. 2013, Vol 96, Num 8, pp 1871-1874, issn 0916-8532, 4 p.Article

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