Titlebook: Smelter Grade Alumina from Bauxite; History, Best Practi Benny E. Raahauge,Fred S. Williams Book 2022 The Editor(s) (if applicable) and The

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Benny E. Raahaugened interest from the community thanks to their high accuracy and interpretable results. However, these shapelet-based methods still have some problems in both shapelet initialization and learning shapelet phases that limit their performances. In this paper, we propose a novel shapelet-based classif

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Carsten Duwee physical knowledge on the system edge is limited and challenging to recover completely. Solving such problems typically requires identifying forward system mapping rules, from system states to the output measurements. However, the forward system identification based on digital twin can hardly prov

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Brady Haneman,Benny E. Raahaugeing assistance across the nation. Despite these efforts, the number of youth still homeless or unstably housed remains very high. Motivated by this fact, we initiate a first study to understand and analyze the current governmental housing systems for homeless youth. In this paper, we aim to provide

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Peter-Hans ter Weerionized recommender system still remains challenging due to a massive collection of items, a huge number of online users, and requirements for recommendations to be responsive to user actions. In this work, we present our relevant, responsive, and scalable deep online ranking system (DORS) that we d

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to learn how to run and control the correct execution of hundreds of models; we need ways to automate the data mining process..I will present these constraints in industrial contexts and show how KXEN has exploited theoretical results (coming from Vladimir Vapnik’s work) to provide answers to the a

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Benny E. Raahauge,Fred S. Williamsty of variables. Our error bounds are multiplicative and significantly improve upon those of the CM sketch, a state-of-the-art approach to estimating probabilities in streams. We evaluate our approximations on synthetic and real-world problems, and report an order of magnitude improvements over the

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George Komlóssy,Caio van Deursen,Benny E. Raahaugeestablish a novel hypergraph framework which is used for characterizing the multiple relationships within a set of samples. Thus, the structural information latent in the data can be more effectively modeled. Secondly, we derive a hypergraph subspace learning view of feature selection which casting

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