Zobrazeno 1 - 10
of 270
pro vyhledávání: '"SUZUKI, YOSHIKI"'
Background: Understanding nuclear shape is a crucial problem in nuclear physics. In particular, determining the sign of quadrupole deformation, i.e., whether prolate or oblate, remains a challenging problem. Purpose: Our aim is to propose a method fo
Externí odkaz:
http://arxiv.org/abs/2402.13832
Autor:
Iida, Tomomichi, Hosojima, Michihiro, Kabasawa, Hideyuki, Yamamoto-Kabasawa, Keiko, Goto, Sawako, Tanaka, Takahiro, Kitamura, Nobutaka, Nakada, Mitsutaka, Itoh, Shino, Ogasawara, Shinya, Kaseda, Ryohei, Suzuki, Yoshiki, Narita, Ichiei, Saito, Akihiko
Publikováno v:
In Journal of Diabetes and Its Complications November 2022 36(11)
Akademický článek
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We introduce a novel sensitivity analysis framework for large scale classification problems that can be used when a small number of instances are incrementally added or removed. For quickly updating the classifier in such a situation, incremental lea
Externí odkaz:
http://arxiv.org/abs/1504.02870
Careful tuning of a regularization parameter is indispensable in many machine learning tasks because it has a significant impact on generalization performances. Nevertheless, current practice of regularization parameter tuning is more of an art than
Externí odkaz:
http://arxiv.org/abs/1502.02344
Autor:
Kimura, Yosuke, Otobe, Yuhei, Suzuki, Mizue, Tanaka, Shu, Kojima, Iwao, Suzuki, Yoshiki, Oyamada, Chihiro, Kobayashi, Daishun, Hamanaka, Koji, Yamada, Minoru
Publikováno v:
Disability & Rehabilitation; Sep2024, Vol. 46 Issue 19, p4377-4383, 7p
Autor:
Murayama, Toshiko, Hosojima, Michihiro, Kabasawa, Hideyuki, Tanaka, Takahiro, Kitamura, Nobutaka, Tanaka, Mai, Kuwahara, Shoji, Suzuki, Yoshiki, Narita, Ichiei, Saito, Akihiko
Publikováno v:
BMC Nutrition; 7/4/2024, Vol. 10 Issue 1, p1-8, 8p
Practical model building processes are often time-consuming because many different models must be trained and validated. In this paper, we introduce a novel algorithm that can be used for computing the lower and the upper bounds of model validation e
Externí odkaz:
http://arxiv.org/abs/1402.2148
Sparse classifiers such as the support vector machines (SVM) are efficient in test-phases because the classifier is characterized only by a subset of the samples called support vectors (SVs), and the rest of the samples (non SVs) have no influence on
Externí odkaz:
http://arxiv.org/abs/1401.6740
Akademický článek
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