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Takafumi Kanamori: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Takafumi Kanamori
    A New Sequential Algorithm for Regression Problems by Using Mixture Distribution. [Citation Graph (0, 0)][DBLP]
    ICANN, 2002, pp:535-540 [Conf]
  2. Takafumi Kanamori, Takashi Takenouchi, Shinto Eguchi, Noboru Murata
    The Most Robust Loss Function for Boosting. [Citation Graph (0, 0)][DBLP]
    ICONIP, 2004, pp:496-501 [Conf]
  3. Noboru Murata, Takashi Takenouchi, Takafumi Kanamori, Shinto Eguchi
    Information Geometry of U-Boost and Bregman Divergence. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2004, v:16, n:7, pp:1437-1481 [Journal]
  4. Ichiro Takeuchi, Yoshua Bengio, Takafumi Kanamori
    Robust Regression with Asymmetric Heavy-Tail Noise Distributions. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2002, v:14, n:10, pp:2469-2496 [Journal]
  5. Takafumi Kanamori, Ichiro Takeuchi
    Conditional mean estimation under asymmetric and heteroscedastic error by linear combination of quantile regressions. [Citation Graph (0, 0)][DBLP]
    Computational Statistics & Data Analysis, 2006, v:50, n:12, pp:3605-3618 [Journal]
  6. Takafumi Kanamori
    Multiclass Boosting Algorithms for Shrinkage Estimators of Class Probability. [Citation Graph (0, 0)][DBLP]
    ALT, 2007, pp:358-372 [Conf]
  7. Takafumi Kanamori
    Pool-based active learning with optimal sampling distribution and its information geometrical interpretation. [Citation Graph (0, 0)][DBLP]
    Neurocomputing, 2007, v:71, n:1-3, pp:353-362 [Journal]

  8. Inlier-Based Outlier Detection via Direct Density Ratio Estimation. [Citation Graph (, )][DBLP]

  9. The Entire Solution Path of Kernel-based Nonparametric Conditional Quantile Estimator. [Citation Graph (, )][DBLP]

  10. Efficient Direct Density Ratio Estimation for Non-stationarity Adaptation and Outlier Detection. [Citation Graph (, )][DBLP]

  11. Direct Density Ratio Estimation with Dimensionality Reduction. [Citation Graph (, )][DBLP]

  12. Mutual information estimation reveals global associations between stimuli and biological processes. [Citation Graph (, )][DBLP]

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