نتایج جستجو برای: kernel sliced inverse regression ksir
تعداد نتایج: 448527 فیلتر نتایج به سال:
Regularization schemes for regression have been widely studied in learning theory and inverse problems. In this paper, we study distribution (DR) which involves two stages of sampling, aims at regressing from probability measures to real-valued responses over a reproducing kernel Hilbert space (RKHS). Recently, theoretical analysis on DR has carried out via ridge several behaviors observed. How...
Embedding probability distributions into a sufficiently rich (characteristic) reproducing kernel Hilbert space enables us to take higher order statistics into account. Characterization also retains effective statistical relation between inputs and outputs in regression and classification. Recent works established conditions for characteristic kernels on groups and semigroups. Here we study char...
We propose an algorithm for exploiting statistical properties of large-scale metadata databases about music titles to answer musicological queries. We introduce two inference schemes called “direct” and “inverse” inference, based on an efficient implementation of a kernel regression approach. We describe an evaluation experiment conducted on a large-scale database of finegrained musical metadat...
Nowadays, Radar systems have many applications and radar imaging is one of the most important of these applications. Inverse Synthetic Aperture Radar (ISAR) is used to form an image from moving targets. Conventional methods use Fourier transform to retrieve Doppler information. However, because of maneuvering of the target, the Doppler spectrum becomes time-varying and the image is blurred. Joi...
This paper considers an aggregator of Electric Vehicles (EVs) who aims to learn the aggregate power his/her fleet while also participating in electricity market. The proposed approach is based on a data-driven inverse optimization (IO) method, which highly nonlinear. To overcome such caveat, we use two-step estimation procedure requires solving two convex programs. Both programs depend penalty ...
Several neural decoding algorithms have successfully converted brain signals into commands to control a computer cursor and prosthetic devices. A majority of decoding methods, such as population vector algorithms (PVA), optimal linear estimators (OLE), and neural networks (NN), are effective in predicting movement kinematics, including movement direction, speed and trajectory but usually requir...
We consider nonparametric regression of a scalar outcome on a covariate when the outcome is missing at random (MAR) given the covariate and other observed auxiliary variables. We propose a class of augmented inverse probability weighted (AIPW) kernel estimating equations for nonparametric regression under MAR. We show that AIPW kernel estimators are consistent when the probability that the outc...
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