메뉴 건너뛰기




Volumn 25, Issue 10, 2013, Pages 2734-2775

Density-difference estimation

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUSTRALIA; COMPUTER PROGRAM; DATA MINING; DIABETES MELLITUS; FACTUAL DATABASE; GERMANY; HUMAN; STATISTICAL MODEL; STATISTICS;

EID: 84877702876     PISSN: 08997667     EISSN: 1530888X     Source Type: Journal    
DOI: 10.1162/NECO_a_00492     Document Type: Article
Times cited : (58)

References (70)
  • 1
    • 0001912663 scopus 로고
    • Two-sample test statistics for measuring discrepancies between two multivariate probability density functions using kernel-based density estimates
    • Anderson, N., Hall, P., & Titterington, D. (1994). Two-sample test statistics for measuring discrepancies between two multivariate probability density functions using kernel-based density estimates. Journal of Multivariate Analysis, 50, 41-54.
    • (1994) Journal of Multivariate Analysis , vol.50 , pp. 41-54
    • Anderson, N.1    Hall, P.2    Titterington, D.3
  • 2
    • 84877752392 scopus 로고    scopus 로고
    • Kernel change-point detection
    • (Tech. Rep. 1202.3878). arXiv
    • Arlot, S., Celisse, A., & Harchaoui, Z. (2012). Kernel change-point detection (Tech. Rep. 1202.3878). arXiv.
    • (2012)
    • Arlot, S.1    Celisse, A.2    Harchaoui, Z.3
  • 4
    • 84943400634 scopus 로고    scopus 로고
    • Non-rigid medical image registration by maximisation of quadratic mutual information
    • In Proceedings of the IEEE 29th Annual Northeast Bioengineering Conference , Piscataway, NJ: IEEE
    • Atif, J., Ripoche, X., & Osorio, A. (2003). Non-rigid medical image registration by maximisation of quadratic mutual information. In Proceedings of the IEEE 29th Annual Northeast Bioengineering Conference (pp. 32-40). Piscataway, NJ: IEEE.
    • (2003) , pp. 32-40
    • Atif, J.1    Ripoche, X.2    Osorio, A.3
  • 5
    • 0012786195 scopus 로고
    • Multiple regression and estimation of the mean of a multivariate normal distribution
    • (Tech. Rep. 51). Stanford, CA: Department of Statistics, Stanford University
    • Baranchik, A. J. (1964). Multiple regression and estimation of the mean of a multivariate normal distribution (Tech. Rep. 51). Stanford, CA: Department of Statistics, Stanford University.
    • (1964)
    • Baranchik, A.J.1
  • 6
    • 0001640740 scopus 로고    scopus 로고
    • Robust and efficient estimation by minimising a density power divergence
    • Basu, A., Harris, I. R., Hjort, N. L., & Jones, M. C. (1998). Robust and efficient estimation by minimising a density power divergence. Biometrika, 85, 549-559.
    • (1998) Biometrika , vol.85 , pp. 549-559
    • Basu, A.1    Harris, I.R.2    Hjort, N.L.3    Jones, M.C.4
  • 8
    • 34547994328 scopus 로고    scopus 로고
    • Discriminative learning for differing training and test distributions
    • In Proceedings of the 24th International Conference on Machine Learning , New York: ACM
    • Bickel, S., Brückner, M., & Scheffer, T. (2007). Discriminative learning for differing training and test distributions. In Proceedings of the 24th International Conference on Machine Learning (pp. 81-88). New York: ACM.
    • (2007) , pp. 81-88
    • Bickel, S.1    Brückner, M.2    Scheffer, T.3
  • 9
    • 0026966646 scopus 로고
    • A training algorithm for optimal margin classifiers
    • In Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory , New York: ACM Press
    • Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. In Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory (pp. 144-152). New York: ACM Press.
    • (1992) , pp. 144-152
    • Boser, B.E.1    Guyon, I.M.2    Vapnik, V.N.3
  • 10
    • 33749252873 scopus 로고    scopus 로고
    • Semi-supervised learning
    • Cambridge, MA: MIT Press
    • Chapelle, O., Schölkopf, B., & Zien, A. (Eds.). (2006). Semi-supervised learning. Cambridge, MA: MIT Press.
    • (2006)
    • Chapelle, O.1    Schölkopf, B.2    Zien, A.3
  • 11
    • 0014710323 scopus 로고
    • On optimum recognition error and reject tradeoff
    • Chow, C. K. (1970). On optimum recognition error and reject tradeoff. IEEE Transactions on Information Theory, 16, 41-46.
    • (1970) IEEE Transactions on Information Theory , vol.16 , pp. 41-46
    • Chow, C.K.1
  • 12
    • 84855232807 scopus 로고    scopus 로고
    • Learning bounds for importance weighting
    • In J. Lafferty, C. K. I.Williams, R. Zemel, J. Shawe-Taylor, & A. Culotta (Eds.), Advances in neural information processing systems, Cambridge, MA: MIT Press
    • Cortes, C., Mansour, Y., & Mohri, M. (2010). Learning bounds for importance weighting. In J. Lafferty, C. K. I.Williams, R. Zemel, J. Shawe-Taylor, & A. Culotta (Eds.), Advances in neural information processing systems, 23 (pp. 442-450). Cambridge, MA: MIT Press.
    • (2010) , vol.23 , pp. 442-450
    • Cortes, C.1    Mansour, Y.2    Mohri, M.3
  • 13
    • 34249753618 scopus 로고
    • Support-vector networks
    • Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20, 273-297.
    • (1995) Machine Learning , vol.20 , pp. 273-297
    • Cortes, C.1    Vapnik, V.2
  • 15
    • 84887368877 scopus 로고    scopus 로고
    • Class-prior change labeling
    • Unpublished research memo
    • du Plessis, M. C. (2013). Class-prior change labeling. Unpublished research memo.
    • (2013)
    • Du Plessis, M.C.1
  • 16
    • 84867122342 scopus 로고    scopus 로고
    • Semi-supervised learning of class balance under class-prior change by distribution matching
    • In Proceedings of 29th International Conference on Machine Learning , Madison, WI: Omnipress
    • du Plessis, M. C., & Sugiyama, M. (2012). Semi-supervised learning of class balance under class-prior change by distribution matching. In Proceedings of 29th International Conference on Machine Learning (pp. 823-830). Madison, WI: Omnipress.
    • (2012) , pp. 823-830
    • Du Plessis, M.C.1    Sugiyama, M.2
  • 17
    • 0003922190 scopus 로고    scopus 로고
    • Pattern classification (2nd ed.)
    • New York:Wiley
    • Duda, R. O., Hart, P. E., & Stork, D. G. (2001). Pattern classification (2nd ed.). New York:Wiley.
    • (2001)
    • Duda, R.O.1    Hart, P.E.2    Stork, D.G.3
  • 18
    • 68249094693 scopus 로고    scopus 로고
    • Highest density difference region estimation with application to flow cytometric data
    • Duong, T., Koch, I., & Wand, M. P. (2009). Highest density difference region estimation with application to flow cytometric data. Biometrical Journal, 51, 504-521.
    • (2009) Biometrical Journal , vol.51 , pp. 504-521
    • Duong, T.1    Koch, I.2    Wand, M.P.3
  • 19
    • 85162560047 scopus 로고    scopus 로고
    • Optimal learning rates for least squares SVMs using gaussian kernels
    • In J. Shawe-Taylor, R. S. Zemel, P. Bartlett, F. C. N. Pereira, & K. Q. Weinberger (Eds.), Advances in neural information processing systems, Red Hook, NY: Curran
    • Eberts, M., & Steinwart, I. (2011). Optimal learning rates for least squares SVMs using gaussian kernels. In J. Shawe-Taylor, R. S. Zemel, P. Bartlett, F. C. N. Pereira, & K. Q. Weinberger (Eds.), Advances in neural information processing systems, 24 (pp. 1539-1547). Red Hook, NY: Curran.
    • (2011) , vol.24 , pp. 1539-1547
    • Eberts, M.1    Steinwart, I.2
  • 20
    • 0003991665 scopus 로고
    • An introduction to the bootstrap
    • Boca Raton, FL: Chapman & Hall/CRC
    • Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. Boca Raton, FL: Chapman & Hall/CRC.
    • (1993)
    • Efron, B.1    Tibshirani, R.J.2
  • 21
    • 0001419823 scopus 로고
    • On the best obtainable asymptotic rates of convergence in estimation of a density function at a point
    • Farrell, R. H. (1972). On the best obtainable asymptotic rates of convergence in estimation of a density function at a point. Annals of Mathematical Statistics, 43, 170-180.
    • (1972) Annals of Mathematical Statistics , vol.43 , pp. 170-180
    • Farrell, R.H.1
  • 22
    • 77953751043 scopus 로고    scopus 로고
    • Quadratic mutual information for dimensionality reduction and classification
    • Proceedings of SPIE, Bellingham, WA: SPIE
    • Gray, D.M., & Principe, J. C. (2010). Quadratic mutual information for dimensionality reduction and classification. Proceedings of SPIE (p. 76960D). Bellingham, WA: SPIE.
    • (2010)
    • Gray, D.M.1    Principe, J.C.2
  • 23
    • 70349847999 scopus 로고    scopus 로고
    • Covariate shift by kernel mean matching
    • In J. Quiñonero, M. Sugiyama, A. Schwaighofer, & N. Lawrence (Eds.), Dataset shift in machine learning , Cambridge, MA: MIT Press
    • Gretton, A., Smola, A., Huang, J., Schmittfull, M., Borgwardt, K., & Schölkopf, B. (2009). Covariate shift by kernel mean matching. In J. Quiñonero, M. Sugiyama, A. Schwaighofer, & N. Lawrence (Eds.), Dataset shift in machine learning (pp. 131-160). Cambridge, MA: MIT Press.
    • (2009) , pp. 131-160
    • Gretton, A.1    Smola, A.2    Huang, J.3    Schmittfull, M.4    Borgwardt, K.5    Schölkopf, B.6
  • 24
    • 0043237580 scopus 로고
    • On nonparametric discrimination using density differences
    • Hall, P., & Wand, M. P. (1988). On nonparametric discrimination using density differences. Biometrika, 75, 541-547.
    • (1988) Biometrika , vol.75 , pp. 541-547
    • Hall, P.1    Wand, M.P.2
  • 25
    • 84858765869 scopus 로고    scopus 로고
    • Kernel change-point analysis
    • In D. Koller, D. Schuurmans, Y. Bengio, & L. Bettou (Eds.), Advances in neural information processing systems, Cambridge, MA: MIT Press
    • Harchaoui, Z., Bach, F., & Moulines, E. (2009). Kernel change-point analysis. In D. Koller, D. Schuurmans, Y. Bengio, & L. Bettou (Eds.), Advances in neural information processing systems, 21 (pp. 609-616). Cambridge, MA: MIT Press.
    • (2009) , vol.21 , pp. 609-616
    • Harchaoui, Z.1    Bach, F.2    Moulines, E.3
  • 26
    • 8844240865 scopus 로고    scopus 로고
    • Nonparametric and semiparametric models
    • Berlin: Springer
    • Härdle, W., Müller, M., Sperlich, S., & Werwatz, A. (2004). Nonparametric and semiparametric models. Berlin: Springer.
    • (2004)
    • Härdle, W.1    Müller, M.2    Sperlich, S.3    Werwatz, A.4
  • 27
    • 0003684449 scopus 로고    scopus 로고
    • The elements of statistical learning: Data mining, inference, and prediction
    • New York: Springer
    • Hastie, T., Tibshirani, R., & Friedman, J. (2001). The elements of statistical learning: Data mining, inference, and prediction. New York: Springer.
    • (2001)
    • Hastie, T.1    Tibshirani, R.2    Friedman, J.3
  • 28
    • 0004245065 scopus 로고
    • Tests of significance
    • Beverly Hills, CA: Sage
    • Henkel, R. E. (1976). Tests of significance. Beverly Hills, CA: Sage.
    • (1976)
    • Henkel, R.E.1
  • 29
    • 84864031047 scopus 로고    scopus 로고
    • Correcting sample selection bias by unlabeled data
    • In B. Schölkopf, J. C. Platt, & T. Hoffmann (Eds.), Advances in neural information processing systems, Cambridge, MA: MIT Press
    • Huang, J., Smola, A., Gretton, A., Borgwardt, K. M., & Schölkopf, B. (2007). Correcting sample selection bias by unlabeled data. In B. Schölkopf, J. C. Platt, & T. Hoffmann (Eds.), Advances in neural information processing systems, 19 (pp. 601-608). Cambridge, MA: MIT Press.
    • (2007) , vol.19 , pp. 601-608
    • Huang, J.1    Smola, A.2    Gretton, A.3    Borgwardt, K.M.4    Schölkopf, B.5
  • 31
    • 84867133788 scopus 로고    scopus 로고
    • Statistical analysis of kernel-based least-squares density-ratio estimation
    • Kanamori, T., Suzuki, T., & Sugiyama, M. (2012). Statistical analysis of kernel-based least-squares density-ratio estimation. Machine Learning, 86, 335-367.
    • (2012) Machine Learning , vol.86 , pp. 335-367
    • Kanamori, T.1    Suzuki, T.2    Sugiyama, M.3
  • 32
    • 84858312965 scopus 로고    scopus 로고
    • Sequential change-point detection based on direct density-ratio estimation
    • Kawahara, Y., & Sugiyama, M. (2012). Sequential change-point detection based on direct density-ratio estimation. Statistical Analysis and Data Mining, 5, 114-127.
    • (2012) Statistical Analysis and Data Mining , vol.5 , pp. 114-127
    • Kawahara, Y.1    Sugiyama, M.2
  • 33
    • 49749093757 scopus 로고    scopus 로고
    • Change-point detection in time-series data based on subspace identification
    • In Proceedings of the 7th IEEE International Conference on Data Mining , Piscataway, NJ: IEEE
    • Kawahara, Y., Yairi, T., & Machida, K. (2007). Change-point detection in time-series data based on subspace identification. In Proceedings of the 7th IEEE International Conference on Data Mining (pp. 559-564). Piscataway, NJ: IEEE.
    • (2007) , pp. 559-564
    • Kawahara, Y.1    Yairi, T.2    Machida, K.3
  • 35
    • 76749089534 scopus 로고    scopus 로고
    • Probability density difference-based active contour for ultrasound image segmentation
    • Liu, B., Cheng, H. D., Huang, J., Tian, J., Tang, X., & Liu, J. (2010). Probability density difference-based active contour for ultrasound image segmentation. Pattern Recognition, 43, 2028-2042.
    • (2010) Pattern Recognition , vol.43 , pp. 2028-2042
    • Liu, B.1    Cheng, H.D.2    Huang, J.3    Tian, J.4    Tang, X.5    Liu, J.6
  • 36
    • 84875257444 scopus 로고    scopus 로고
    • Change-point detection in time-series data by relative density-ratio estimation
    • Liu, S., Yamada, M., Collier, N., & Sugiyama, M. (2013). Change-point detection in time-series data by relative density-ratio estimation. Neural Networks, 43, 72-83.
    • (2013) Neural Networks , vol.43 , pp. 72-83
    • Liu, S.1    Yamada, M.2    Collier, N.3    Sugiyama, M.4
  • 38
    • 77958588617 scopus 로고    scopus 로고
    • Estimating divergence functionals and the likelihood ratio by convex risk minimization
    • Nguyen, X., Wainwright, M. J., & Jordan, M. I. (2010). Estimating divergence functionals and the likelihood ratio by convex risk minimization. IEEE Transactions on Information Theory, 56, 5847-5861.
    • (2010) IEEE Transactions on Information Theory , vol.56 , pp. 5847-5861
    • Nguyen, X.1    Wainwright, M.J.2    Jordan, M.I.3
  • 39
    • 0001473437 scopus 로고
    • On the estimation of a probability density function and mode
    • Parzen, E. (1962). On the estimation of a probability density function and mode. Annals of Mathematical Statistics, 33, 1065-1076.
    • (1962) Annals of Mathematical Statistics , vol.33 , pp. 1065-1076
    • Parzen, E.1
  • 40
    • 52649098412 scopus 로고    scopus 로고
    • Kullback-Leibler divergence estimation of continuous distributions
    • In Proceedings of IEEE International Symposium on Information Theory , Piscataway, NJ: IEEE
    • Pérez-Cruz, F. (2008). Kullback-Leibler divergence estimation of continuous distributions. In Proceedings of IEEE International Symposium on Information Theory (pp. 1666-1670). Piscataway, NJ: IEEE.
    • (2008) , pp. 1666-1670
    • Pérez-Cruz, F.1
  • 41
    • 0042600863 scopus 로고    scopus 로고
    • Inferences for case-control and semiparametric two-sample density ratio models
    • Qin, J. (1998). Inferences for case-control and semiparametric two-sample density ratio models. Biometrika, 85, 619-630.
    • (1998) Biometrika , vol.85 , pp. 619-630
    • Qin, J.1
  • 42
    • 0003436776 scopus 로고
    • Linear statistical inference and its applications
    • NewYork: Wiley
    • Rao, C. R. (1965). Linear statistical inference and its applications. NewYork: Wiley.
    • (1965)
    • Rao, C.R.1
  • 43
    • 9444250658 scopus 로고    scopus 로고
    • Regularized least-squares classification
    • In J.A.K. Suykens, G.Horvath, S. Basu, C. Micchelli, & J.Vandewalle (Eds.), Advances in learning theory: Methods, models and applications , Amsterdam: IOS Press
    • Rifkin, R., Yeo, G., & Poggio, T. (2003). Regularized least-squares classification. In J.A.K. Suykens, G.Horvath, S. Basu, C. Micchelli, & J.Vandewalle (Eds.), Advances in learning theory: Methods, models and applications (pp. 131-154). Amsterdam: IOS Press.
    • (2003) , pp. 131-154
    • Rifkin, R.1    Yeo, G.2    Poggio, T.3
  • 44
    • 0004267646 scopus 로고
    • Convex analysis
    • Princeton, NJ: Princeton University Press
    • Rockafellar, R. T. (1970). Convex analysis. Princeton, NJ: Princeton University Press.
    • (1970)
    • Rockafellar, R.T.1
  • 45
    • 0036134369 scopus 로고    scopus 로고
    • Adjusting the outputs of a classifier to new a priori probabilities: A simple procedure
    • Saerens, M., Latinne, P., & Decaestecker, C. (2002). Adjusting the outputs of a classifier to new a priori probabilities: A simple procedure. Neural Computation, 14, 21-41.
    • (2002) Neural Computation , vol.14 , pp. 21-41
    • Saerens, M.1    Latinne, P.2    Decaestecker, C.3
  • 46
    • 0035419755 scopus 로고    scopus 로고
    • Parametric statistical modeling by minimum integrated square error
    • Scott, D. W. (2001). Parametric statistical modeling by minimum integrated square error. Technometrics, 43, 274-285.
    • (2001) Technometrics , vol.43 , pp. 274-285
    • Scott, D.W.1
  • 47
    • 77954029241 scopus 로고    scopus 로고
    • Information divergence estimation based on data-dependent partitions
    • Silva, J., & Narayanan, S. S. (2010). Information divergence estimation based on data-dependent partitions. Journal of Statistical Planning and Inference, 140, 3180-3198.
    • (2010) Journal of Statistical Planning and Inference , vol.140 , pp. 3180-3198
    • Silva, J.1    Narayanan, S.S.2
  • 48
    • 0003443397 scopus 로고
    • Density estimation for statistics and data analysis
    • London: Chapman and Hall
    • Silverman, B. W. (1986). Density estimation for statistics and data analysis. London: Chapman and Hall.
    • (1986)
    • Silverman, B.W.1
  • 49
    • 68949128341 scopus 로고    scopus 로고
    • Support vector machines
    • NewYork: Springer
    • Steinwart, I., & Christmann, A. (2008). Support vector machines. NewYork: Springer.
    • (2008)
    • Steinwart, I.1    Christmann, A.2
  • 50
    • 34247197035 scopus 로고    scopus 로고
    • Fast rates for support vector machines using gaussian kernels
    • Steinwart, I., & Scovel, C. (2007). Fast rates for support vector machines using gaussian kernels. Annals of Statistics, 35, 575-607.
    • (2007) Annals of Statistics , vol.35 , pp. 575-607
    • Steinwart, I.1    Scovel, C.2
  • 51
    • 77957851853 scopus 로고    scopus 로고
    • Superfast-trainable multi-class probabilistic classifier by leastsquares posterior fitting
    • Sugiyama, M. (2010). Superfast-trainable multi-class probabilistic classifier by leastsquares posterior fitting. IEICE Transactions on Information and Systems, E93-D, 2690-2701.
    • (2010) IEICE Transactions on Information and Systems , vol.E93-D , pp. 2690-2701
    • Sugiyama, M.1
  • 52
    • 84865369611 scopus 로고    scopus 로고
    • Machine learning in non-stationary environments: Introduction to covariate shift adaptation
    • Cambridge, MA: MIT Press
    • Sugiyama, M., & Kawanabe, M. (2012). Machine learning in non-stationary environments: Introduction to covariate shift adaptation. Cambridge, MA: MIT Press.
    • (2012)
    • Sugiyama, M.1    Kawanabe, M.2
  • 53
    • 70649085072 scopus 로고    scopus 로고
    • Dimensionality reduction for density ratio estimation in high-dimensional spaces
    • Sugiyama, M., Kawanabe, M., & Chui, P. L. (2010). Dimensionality reduction for density ratio estimation in high-dimensional spaces. Neural Networks, 23, 44-59.
    • (2010) Neural Networks , vol.23 , pp. 44-59
    • Sugiyama, M.1    Kawanabe, M.2    Chui, P.L.3
  • 55
    • 84926087908 scopus 로고    scopus 로고
    • Density ratio estimation in machine learning
    • Cambridge: Cambridge University Press
    • Sugiyama, M., Suzuki, T., & Kanamori, T. (2012a). Density ratio estimation in machine learning. Cambridge: Cambridge University Press.
    • (2012)
    • Sugiyama, M.1    Suzuki, T.2    Kanamori, T.3
  • 56
    • 84865400174 scopus 로고    scopus 로고
    • Density ratio matching under the Bregman divergence: A unified framework of density ratio estimation
    • Sugiyama, M., Suzuki, T., & Kanamori, T. (2012b). Density ratio matching under the Bregman divergence: A unified framework of density ratio estimation. Annals of the Institute of Statistical Mathematics, 64, 1009-1044.
    • (2012) Annals of the Institute of Statistical Mathematics , vol.64 , pp. 1009-1044
    • Sugiyama, M.1    Suzuki, T.2    Kanamori, T.3
  • 57
    • 84873181533 scopus 로고    scopus 로고
    • Density-difference estimation
    • In P. Bartlett, F.C.N. Pereira, C.J.C. Burgess, L. Bettou, & K. Q. Weinberger (Eds.), Advances in neural information processing systems, 25. Red Hook, NY: Curran
    • Sugiyama, M., Suzuki, T., Kanamori, T., du Plessis, M. C., Liu, S., & Takeuchi, I. (2012). Density-difference estimation. In P. Bartlett, F.C.N. Pereira, C.J.C. Burgess, L. Bettou, & K. Q. Weinberger (Eds.), Advances in neural information processing systems, 25. Red Hook, NY: Curran.
    • (2012)
    • Sugiyama, M.1    Suzuki, T.2    Kanamori, T.3    Du Plessis, M.C.4    Liu, S.5    Takeuchi, I.6
  • 60
    • 79251612332 scopus 로고    scopus 로고
    • Direct density-ratio estimation with dimensionality reduction via leastsquares hetero-distributional subspace search
    • Sugiyama, M., Yamada, M., von Bünau, P., Suzuki, T., Kanamori, T., & Kawanabe, M. (2011). Direct density-ratio estimation with dimensionality reduction via leastsquares hetero-distributional subspace search. Neural Networks, 24, 183-198.
    • (2011) Neural Networks , vol.24 , pp. 183-198
    • Sugiyama, M.1    Yamada, M.2    Von Bünau, P.3    Suzuki, T.4    Kanamori, T.5    Kawanabe, M.6
  • 61
    • 84877714816 scopus 로고    scopus 로고
    • Sufficient dimension reduction via squared-loss mutual information estimation
    • Suzuki, T., & Sugiyama, M. (2013). Sufficient dimension reduction via squared-loss mutual information estimation. Neural Computation, 25, 725-758.
    • (2013) Neural Computation , vol.25 , pp. 725-758
    • Suzuki, T.1    Sugiyama, M.2
  • 62
    • 33644653840 scopus 로고    scopus 로고
    • A unifying framework for detecting outliers and change points from non-stationary time series data
    • Takeuchi, Y., & Yamanishi, K. (2006). A unifying framework for detecting outliers and change points from non-stationary time series data. IEEE Transactions on Knowledge and Data Engineering, 18, 482-489.
    • (2006) IEEE Transactions on Knowledge and Data Engineering , vol.18 , pp. 482-489
    • Takeuchi, Y.1    Yamanishi, K.2
  • 63
    • 1942450610 scopus 로고    scopus 로고
    • Feature extraction by non-parametric mutual information maximization
    • Torkkola, K. (2003). Feature extraction by non-parametric mutual information maximization. Journal of Machine Learning Research, 3, 1415-1438.
    • (2003) Journal of Machine Learning Research , vol.3 , pp. 1415-1438
    • Torkkola, K.1
  • 64
    • 0003991806 scopus 로고    scopus 로고
    • Statistical learning theory
    • New York:Wiley
    • Vapnik, V. N. (1998). Statistical learning theory. New York:Wiley.
    • (1998)
    • Vapnik, V.N.1
  • 66
    • 26444495559 scopus 로고    scopus 로고
    • Divergence estimation of continuous distributions based on data-dependent partitions
    • Wang, Q., Kulkarmi, S. R., & Verdu, S. (2005). Divergence estimation of continuous distributions based on data-dependent partitions. IEEE Transactions on Information Theory, 51, 3064-3074.
    • (2005) IEEE Transactions on Information Theory , vol.51 , pp. 3064-3074
    • Wang, Q.1    Kulkarmi, S.R.2    Verdu, S.3
  • 67
    • 80055057089 scopus 로고    scopus 로고
    • Direct density-ratio estimation with dimensionality reduction via hetero-distributional subspace analysis
    • In Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence , San Francisco: AAAI Press
    • Yamada, M., & Sugiyama, M. (2011). Direct density-ratio estimation with dimensionality reduction via hetero-distributional subspace analysis. In Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence (pp. 549-554). San Francisco: AAAI Press.
    • (2011) , pp. 549-554
    • Yamada, M.1    Sugiyama, M.2
  • 68
    • 84877704150 scopus 로고    scopus 로고
    • Relative density-ratio estimation for robust distribution comparison
    • Yamada, M., Suzuki, T., Kanamori, T., Hachiya, H., & Sugiyama, M. (2013). Relative density-ratio estimation for robust distribution comparison. Neural Computation, 25, 1324-1370.
    • (2013) Neural Computation , vol.25 , pp. 1324-1370
    • Yamada, M.1    Suzuki, T.2    Kanamori, T.3    Hachiya, H.4    Sugiyama, M.5
  • 69
    • 84887357046 scopus 로고    scopus 로고
    • Detection of activities and events without explicit categorization
    • IPSJ Transactions on Mathematical Modeling and Its Applications
    • Yamanaka, M., Matsugu, M., & Sugiyama, M. (forthcoming a). Detection of activities and events without explicit categorization. IPSJ Transactions on Mathematical Modeling and Its Applications.
    • Yamanaka, M.1    Matsugu, M.2    Sugiyama, M.3
  • 70
    • 84887334191 scopus 로고    scopus 로고
    • Salient object detection based on direct density-ratio estimation
    • IPSJ Transactions on Mathematical Modeling and Its Applications
    • Yamanaka, M., Matsugu, M., & Sugiyama, M. (forthcoming b). Salient object detection based on direct density-ratio estimation. IPSJ Transactions on Mathematical Modeling and Its Applications.
    • Yamanaka, M.1    Matsugu, M.2    Sugiyama, M.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.