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Yoshikazu Terada
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2020 – today
- 2023
- [j3]Xin Guan, Yoshikazu Terada:
Sparse kernel k-means for high-dimensional data. Pattern Recognit. 144: 109873 (2023) - 2022
- [i4]Daniel J. W. Touw, Patrick J. F. Groenen, Yoshikazu Terada:
Convex Clustering through MM: An Efficient Algorithm to Perform Hierarchical Clustering. CoRR abs/2211.01877 (2022) - 2021
- [c5]Bingyuan Zhang, Jie Chen, Yoshikazu Terada:
Dynamic visualization for L1 fusion convex clustering in near-linear time. UAI 2021: 515-524 - 2020
- [j2]Yoshikazu Terada, Ryoma Hirose:
Fast generalization error bound of deep learning without scale invariance of activation functions. Neural Networks 129: 344-358 (2020) - [c4]Jen Ning Lim, Makoto Yamada, Wittawat Jitkrittum, Yoshikazu Terada, Shigeyuki Matsui, Hidetoshi Shimodaira:
More Powerful Selective Kernel Tests for Feature Selection. AISTATS 2020: 820-830
2010 – 2019
- 2019
- [c3]Yoshikazu Terada, Michio Yamamoto:
Kernel Normalized Cut: a Theoretical Revisit. ICML 2019: 6206-6214 - [i3]Yoshikazu Terada, Ryoma Hirose:
Fast generalization error bound of deep learning without scale invariance of activation functions. CoRR abs/1907.10900 (2019) - [i2]Jen Ning Lim, Makoto Yamada, Wittawat Jitkrittum, Yoshikazu Terada, Shigeyuki Matsui, Hidetoshi Shimodaira:
More Powerful Selective Kernel Tests for Feature Selection. CoRR abs/1910.06134 (2019) - 2014
- [j1]Michio Yamamoto, Yoshikazu Terada:
Functional factorial K-means analysis. Comput. Stat. Data Anal. 79: 133-148 (2014) - [c2]Yoshikazu Terada, Ulrike von Luxburg:
Local Ordinal Embedding. ICML 2014: 847-855 - 2013
- [i1]Yoshikazu Terada:
Clustering for high-dimension, low-sample size data using distance vectors. CoRR abs/1312.3386 (2013) - 2011
- [c1]Yoshikazu Terada, Hiroshi Yadohisa:
Multidimensional scaling with the nested hypersphere model for percentile dissimilarities. Complex Adaptive Systems 2011: 364-369
Coauthor Index
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