Ichiro Takeuchi

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In regression, the desired estimate of y|x is not always given by a conditional mean, although this is most common. Sometimes one wants to obtain a good estimate that satisfies the property that a proportion, τ , of y|x, will be below the estimate. For τ = 0.5 this is an estimate of the median. What might be called median regression, is subsumed under the(More)
Estimating the conditional mean of an input-output relation is the goal of regression. However, regression analysis is not sufficiently informative if the conditional distribution has multi-modality, is highly asymmetric, or contains heteroscedastic noise. In such scenarios, estimating the conditional distribution itself would be more useful. In this paper,(More)
Clinical outcome of patients with high-grade ccRCC (clear cell renal cell carcinoma) remains still poor despite recent advances in treatment strategies. Molecular mechanism of pathogenesis in developing high-grade ccRCC must be clarified. In the present study, we found that SAV1 was significantly downregulated with copy number loss in high-grade ccRCCs.(More)
This paper gives a comprehensive review on the advances in the field of scanning evanescent microwave microscopy, as a high-throughput characterization tool for electrical properties. Theoretical model analyses used for performing quantitative non-destructive characterization of various materials are presented. Examples of applications of the microwave(More)
Estimating insurance premia from data is a difficult regression problem for several reasons: the large number of variables, many of which are .discrete, and the very peculiar shape of the noise distribution, asymmetric with fat tails, with a large majority zeros and a few unreliable and very large values. We compare several machine learning methods for(More)
In the present study, we report the concentrations of 21 trace elements (V, Cr, Mn, Co, Cu, Zn, As, Se, Rb, Sr, Mo, Ag, Cd, Sn, Sb, Cs, Ba, Hg, Tl, Pb, and Bi), as well as the results of the analysis of stable carbon and nitrogen isotopes, of the various biota that make up the food web in the main stream of the Mekong Delta near Can Tho, South Vietnam. A(More)
We propose a multiple incremental decremental algorithm of support vector machines (SVM). In online learning, we need to update the trained model when some new observations arrive and/or some observations become obsolete. If we want to add or remove single data point, conventional single incremental decremental algorithm can be used to update the model(More)
We address the problem of estimating the difference between two probability densities. A naive approach is a two-step procedure of first estimating two densities separately and then computing their difference. However, this procedure does not necessarily work well because the first step is performed without regard to the second step, and thus a small(More)
In this paper, we claim that some of the nonsupport vectors (non-SVs) that have no influence on the SVM classifier can be screened out prior to the training phase in pathwise SVM computation scenario, in which one is asked to train a sequence (or path) of SVM classifiers for different regularization parameters. Based on a recently proposed framework(More)
In statistical pattern recognition, it is important to avoid density estimation since density estimation is often more difficult than pattern recognition itself. Following this idea—known as Vapnik’s principle, a statistical data processing framework that employs the ratio of two probability density functions has been developed recently and is gathering a(More)