Results 41 to 50 of about 179,741 (181)
Fast Algorithms for Constructing Maximum Entropy Summary Trees [PDF]
, 2014 Karloff? and Shirley recently proposed summary trees as a new way to visualize large rooted trees (Eurovis 2013) and gave algorithms for generating a maximum-entropy k-node summary tree of an input n-node rooted tree.
J. Naudts +2 more
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Distributed greedy pursuit algorithms [PDF]
Signal Processing, 2014 For compressed sensing over arbitrarily connected networks, we consider the problem of estimating underlying sparse signals in a distributed manner. We introduce a new signal model that helps to describe inter-signal correlation among connected nodes.
Dennis Sundman +2 more
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Greedy Firefly Algorithm for Optimizing Job Scheduling in IoT Grid Computing
Sensors, 2022 The Internet of Things (IoT) is defined as interconnected digital and mechanical devices with intelligent and interactive data transmission features over a defined network.
Adil Yousif +6 more
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On the generalized Approximate Weak Chebyshev Greedy Algorithm
, 2016 In this paper we study greedy approximation in Banach spaces. We discuss a modification of the Weak Chebyshev Greedy Algorithm, in which steps of the algorithm can be executed imprecisely.
Dereventsov, Anton
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Greedy algorithms: a review and open problems
Journal of Inequalities and ApplicationsGreedy algorithms are a fundamental class of mathematics and computer science algorithms, defined by their iterative approach of making locally optimal decisions to approximate global optima. In this review, we focus on two greedy algorithms.
Andrea García
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On the rank functions of $\mathcal{H}$-matroids
, 2016 The notion of $\mathcal{H}$-matroids was introduced by U. Faigle and S. Fujishige in 2009 as a general model for matroids and the greedy algorithm. They gave a characterization of $\mathcal{H}$-matroids by the greedy algorithm.
Sano, Yoshio
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A greedy stacking algorithm for model ensembling and domain weighting
BMC Research Notes, 2020 Objective Because it is impossible to know which statistical learning algorithm performs best on a prediction task, it is common to use stacking methods to ensemble individual learners into a more powerful single learner.
Christoph F. Kurz +2 more
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StaticGreedy: solving the scalability-accuracy dilemma in influence maximization
, 2013 Influence maximization, defined as a problem of finding a set of seed nodes to trigger a maximized spread of influence, is crucial to viral marketing on social networks.
Cheng, Suqi +4 more
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Journal of Advance Research in Computer Science & Engineering (ISSN: 2456-3552), 2015 This paper describes the basic technological aspects of algorithm, algorithmic efficiency and Greedy algorithm. Algorithmic efficiency is the property of an algorithm which relate to the amount of resources use by the algorithm in computer sciences. An algorithm is considered efficient if its resource consumption (or computational cost) is at or below ...
Abhishek Jain +2 more
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On Greedy Algorithms for Binary de Bruijn Sequences
, 2020 We propose a general greedy algorithm for binary de Bruijn sequences, called Generalized Prefer-Opposite (GPO) Algorithm, and its modifications. By identifying specific feedback functions and initial states, we demonstrate that most previously-known ...
Chang, Zuling +2 more
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