Search Results - "Hagiwara, Katsuyuki"

Prediction-accuracy improvement of neural network to ferromagnetic multilayers by Gaussian data augmentation and ensemble learning by Nawa, Kenji, Hagiwara, Katsuyuki, Nakamura, Kohji

“…In materials informatics using machine learning and density functional theory (DFT) calculations, it is often hard to obtain enough database due to extremely…”
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Pregnancy Rates after Hysteroscopic Endometrial Polypectomy versus Endometrial Curettage Polypectomy: A Retrospective Study by Nishioka, Mikiko, Maezawa, Tadashi, Takeuchi, Hiroki, Hagiwara, Katsuyuki, Tarui, Sachiyo, Sakamoto, Mito, Takayama, Erina, Yajima, Hideaki, Kondo, Eiji, Kawato, Hiroaki, Minoura, Hiroyuki, Sugaya, Ken, Fukuda, Aisaku, Ikeda, Tomoaki

“…Background and Objectives: A relationship between endometrial polypectomy and in vitro fertilization (IVF) pregnancy outcomes has been reported; however, only…”
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Relation between weight size and degree of over-fitting in neural network regression by Hagiwara, Katsuyuki, Fukumizu, Kenji

“…This paper investigates the relation between over-fitting and weight size in neural network regression. The over-fitting of a network to Gaussian noise is…”
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Investigation of an efficient method of oocyte retrieval by dual stimulation for patients with cancer by Takeuchi, Hiroki, Maezawa, Tadashi, Hagiwara, Katsuyuki, Horage, Yuki, Hanada, Tetsuro, Haipeng, Huang, Sakamoto, Mito, Nishioka, Mikiko, Takayama, Erina, Terada, Kento, Kondo, Eiji, Takai, Yasushi, Suzuki, Nao, Ikeda, Tomoaki

“…Purpose To examine the optimal timing of second ovarian stimulation using the dual stimulation method for good ovarian responders with cancer undergoing oocyte…”
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On scaling of soft-thresholding estimator by Hagiwara, Katsuyuki

“…LASSO is known to have a problem of excessive shrinkage at a sparse representation. To analyze this problem in detail, in this paper, we consider a positive…”
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Upper bound of the expected training error of neural network regression for a Gaussian noise sequence by Hagiwara, Katsuyuki, Hayasaka, Taichi, Toda, Naohiro, Usui, Shiro, Kuno, Kazuhiro

“…In neural network regression problems, often referred to as additive noise models, NIC (Network Information Criterion) has been proposed as a general model…”
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A semi-supervised learning using over-parameterized regression by Hagiwara, Katsuyuki

“…Semi-supervised learning (SSL) is an important theme in machine learning, in which we have a few labeled samples and many unlabeled samples. In this paper, for…”
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On gradient descent training under data augmentation with on-line noisy copies by Hagiwara, Katsuyuki

“…In machine learning, data augmentation (DA) is a technique for improving the generalization performance. In this paper, we mainly considered gradient descent…”
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Bridging between soft and hard thresholding by scaling by Hagiwara, Katsuyuki

“…In this article, we developed and analyzed a thresholding method in which soft thresholding estimators are independently expanded by empirical scaling values…”
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Radical Density Measurement at Low-Pressure Discharge Denitrification by Appearance Mass Spectrometry by Ito, Kohei, Hagiwara, Katsuyuki, Nakaura, Hiroyuki, Tanaka, Hidekazu, Onda, Kazuo

“…In discharge denitrification, radical production by electron collision with combustion gas is a key process which determines the denitrification process and…”
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A consistent model selection for orthogonal regression under component-wise shrinkage by HAGIWARA, Katsuyuki

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On an improvement of LASSO by scaling by Hagiwara, Katsuyuki

“…A sparse modeling is a major topic in machine learning and statistics. LASSO (Least Absolute Shrinkage and Selection Operator) is a popular sparse modeling…”
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A consistent model selection for orthogonal regression under component-wise shrinkage by Hagiwara, Katsuyuki

“…Several authors developed a series of model selection criteria for determining the major frequency components in harmonic analysis. In this paper, we…”
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Adaptive scaling for soft-thresholding estimator by Hagiwara, Katsuyuki

“…Soft-thresholding is a sparse modeling method that is typically applied to wavelet denoising in statistical signal processing and analysis. It has a single…”
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Regularization learning, early stopping and biased estimator by Hagiwara, Katsuyuki

“…In this article, we present a unified statistical interpretation of regularization learning and early stopping for linear networks in the context of…”
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On a training scheme based on orthogonalization and thresholding for a nonparametric regression problem by Hagiwara, Katsuyuki

“…For a nonparametric regression problem, we have been proposed a training scheme based on orthogonalization and thresholding, in which a machine is assumed to…”
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Regularization learning and early stopping in linear networks by HAGIWARA, Katsuyuki, KUNO, Kazuhiro

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On the problem in model selection of neural network regression in overrealizable scenario by HAGIWARA, Katsuyuki, KUNO, Kazuhiro, USUI, Shiro

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On the problem of applying AIC to determine the structure of a layered feed-forward neural network by Hagiwara, Katsuyuki, Toda, Naohiro, Usui, Shiro

“…AIC (Akaike's Information Criterion) has been thought to be effective to determine an optimal structure of layered feed-forward neural networks. However, it…”
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Regularization learning and early stopping in linear networks by Hagiwara, K., Kuno, K.

“…Generally, learning is performed so as to minimize the sum of squared errors between network outputs and training data. Unfortunately, this procedure does not…”
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