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Titlebook: Machine Learning and Knowledge Discovery in Databases: Research Track; European Conference, Danai Koutra,Claudia Plant,Francesco Bonchi Con

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Adacket: ADAptive Convolutional KErnel Transform for Multivariate Time Series Classificationd also rely on the trial and error design of convolutional kernels, limiting comprehensive design space exploration. This hinders fully exploiting convolutional kernels for feature extraction from multivariate time series (MTS) data. To address this issue, we propose a novel method called Adaptive C
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Estimating Dynamic Time Warping Distance Between Time Series with Missing DataDTW cannot handle missing values, and simple fixes (e.g., dropping missing values, or interpolating) fail when entire intervals are missing, as is often the case with, e.g., temporary sensor or communication failures. There is hardly any research on how to address this problem. In this paper, we pro
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Weighted Multivariate Mean Reversion for Online Portfolio Selectionlio selection has received increasing attention from both AI and machine learning communities. Mean reversion is an essential property of stock performance. Hence, most state-of-the-art online portfolio strategies have been built based on this. Though they succeed in specific datasets, most of the e
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ey et al., 3D microscopy deconvolution using Richardson-Lucy algorithm with total variation regularization, 2004), positron emission tomography (PET; Vardi et al., J Am Stat Assoc 80:8–20, 1985), or astronomical imaging (Lantéri and Theys, EURASIP J Appl Signal Processing 15:2500–2513, 2005). Here w
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Cheng Liang,Di Wu,Yi He,Teng Huang,Zhong Chen,Xin Luoatabase design to show how design affects how you approach y.Learn to write SQL queries to select and analyze data, and improve your ability to manipulate data. This book will help you take your existing skills to the next level...Author Mark Simon kicks things off with a quick review of basic SQL k
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