His primary areas of study are Kernel, Reproducing kernel Hilbert space, Kernel embedding of distributions, Kernel method and Embedding. His study looks at the relationship between Kernel and fields such as Conditional probability distribution, as well as how they intersect with chemical problems. His Reproducing kernel Hilbert space study integrates concerns from other disciplines, such as Probability distribution, Applied mathematics and Probability measure.
The Kernel embedding of distributions study combines topics in areas such as Polynomial kernel, Algorithm and Variable kernel density estimation. His Kernel method study combines topics from a wide range of disciplines, such as Statistical theory, Statistical learning theory, Deep learning and Theoretical computer science. In Embedding, he works on issues like Discrete mathematics, which are connected to Pure mathematics.
Kernel, Reproducing kernel Hilbert space, Artificial intelligence, Applied mathematics and Kernel embedding of distributions are his primary areas of study. His Kernel research includes elements of Algorithm, Hilbert space and Kernel. His Reproducing kernel Hilbert space research includes themes of Discrete mathematics, Probability measure, Probability distribution, Statistical distance and Embedding.
The concepts of his Artificial intelligence study are interwoven with issues in Machine learning and Pattern recognition. His Applied mathematics research incorporates themes from Function and Graph. The various areas that he examines in his Kernel embedding of distributions study include Kernel regression, Polynomial kernel, Kernel principal component analysis and Variable kernel density estimation.
His main research concerns Artificial intelligence, Applied mathematics, Kernel, Inference and Set. His work carried out in the field of Artificial intelligence brings together such families of science as Machine learning and Pattern recognition. His Applied mathematics research is multidisciplinary, incorporating perspectives in Reproducing kernel Hilbert space, Minimax, Estimator, Goodness of fit and Rate of convergence.
His Reproducing kernel Hilbert space research focuses on Constant and how it relates to Representation and Function. Kenji Fukumizu undertakes interdisciplinary study in the fields of Kernel and Persistence through his works. His study on Inference also encompasses disciplines like
The scientist’s investigation covers issues in Applied mathematics, Gradient descent, Tree, Inference and Set. Kenji Fukumizu integrates Applied mathematics with Gaussian quadrature in his research. His Gradient descent research is multidisciplinary, incorporating elements of Stability, Generator, Smoothness, Divergence and Mathematical optimization.
His research in Tree intersects with topics in Polytope, Combinatorics, Hierarchical clustering, Cluster analysis and Pattern recognition. His Inference study also includes fields such as
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Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces
Kenji Fukumizu;Francis R. Bach;Michael I. Jordan.
international conference on artificial intelligence and statistics (2004)
A Kernel Statistical Test of Independence
Arthur Gretton;Kenji Fukumizu;Choon H. Teo;Le Song.
neural information processing systems (2007)
Hilbert Space Embeddings and Metrics on Probability Measures
Bharath K. Sriperumbudur;Arthur Gretton;Kenji Fukumizu;Bernhard Schölkopf.
Journal of Machine Learning Research (2010)
Kernel Measures of Conditional Dependence
Kenji Fukumizu;Arthur Gretton;Xiaohai Sun;Bernhard Schölkopf.
neural information processing systems (2007)
Optimal kernel choice for large-scale two-sample tests
Arthur Gretton;Dino Sejdinovic;Heiko Strathmann;Sivaraman Balakrishnan.
neural information processing systems (2012)
Kernel Mean Embedding of Distributions: A Review and Beyond
Krikamol Muandet;Kenji Fukumizu;Bharath K. Sriperumbudur;Bernhard Schölkopf.
Universality, Characteristic Kernels and RKHS Embedding of Measures
Bharath K. Sriperumbudur;Kenji Fukumizu;Gert R. G. Lanckriet.
Journal of Machine Learning Research (2011)
Hilbert space embeddings of conditional distributions with applications to dynamical systems
Le Song;Jonathan Huang;Alex Smola;Kenji Fukumizu.
international conference on machine learning (2009)
Adaptive Method of Realizing Natural Gradient Learning for Multilayer Perceptrons
Shun-Ichi Amari;Hyeyoung Park;Kenji Fukumizu.
Neural Computation (2000)
On the empirical estimation of integral probability metrics
Bharath K. Sriperumbudur;Kenji Fukumizu;Arthur Gretton;Bernhard Schoelkopf.
Electronic Journal of Statistics (2012)
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