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检索条件"主题词=algorithm selection"

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FLOPs as a Discriminant for Dense Linear Algebra algorithms 51

FLOPs as a Discriminant for Dense Linear Algebra Algorithms

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51st International Conference on Parallel Processing (ICPP)

作者： Lopez, Francisco Karlsson, Lars Bientinesi, Paolo Umea Univ Umea Sweden

ISBN: (纸本)9781450397339

Expressions that involve matrices and vectors, known as linear algebra expressions, are commonly evaluated through a sequence of invocations to highly optimised kernels provided in libraries such as BLAS and LAPACK. A sequence of kernels represents an algorithm, and in general, because of associativity, algebraic identities, and multiple kernels, one expression can be evaluated via many different algorithms. These algorithms are all mathematically equivalent (i.e., in exact arithmetic, they all compute the same result), but often differ noticeably in terms of execution time. When faced with a decision, high-level languages, libraries, and tools such as Julia, Armadillo, and Linnea choose by selecting the algorithm that minimises the FLOP count. In this paper, we test the validity of the FLOP count as a discriminant for dense linear algebra algorithms, analysing "anomalies": problem instances for which the fastest algorithm does not perform the least number of FLOPs. To do so, we focused on relatively simple expressions and analysed when and why anomalies occurred. We found that anomalies exist and tend to cluster into large contiguous regions. For one expression anomalies were rare, whereas for the other they were abundant. We conclude that FLOPs is not a sufficiently dependable discriminant even when building algorithms with highly optimised kernels. Plus, most of the anomalies remained as such even after filtering out the inter-kernel cache effects. We conjecture that combining FLOP counts with kernel performance models will significantly improve our ability to choose optimal algorithms.

关键词： linear algebra algorithm selection scientific computing

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A New Evaluation Method for Medical Image Information Hiding Techniques 42

A New Evaluation Method for Medical Image Information Hiding...

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42nd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Eze, Peter Parampalli, Udaya Evans, Robin Liu, Dongxi Univ Melbourne Comp & Informat Syst Melbourne Vic Australia CSIRO Data61 Distributed & Software Syst Sydney NSW Australia

ISBN: (纸本)9781728119908

Medical image scans and associated electronic medical records (EMR) could be stored locally or transmitted for use in autodiagnosis and remote healthcare in teleradiology. Hence, they require security against unauthorised access and modification. Among other means of providing this security, information hiding (IH) techniques have gained relevance especially for open networks that are prone to active attacks. However, the evaluation of the suitability of these IH algorithms in terms of preserving medical image diagnostic features is currently limited to signal processing parameters. This paper re-interprets existing evaluation parameters and provides a new framework that allows dynamic selection of medical image IH (watermarking and steganography) security algorithms. Specifically, criteria that capture medical statistics used in the diagnosis and monitoring of patients were incorporated. These criteria and framework were validated on the Pneumonia Chest Xray dataset (used in a Kaggle Competition) using three selected IH algorithms that offer privacy and image tamper detection.

关键词： medical image Information hiding algorithm selection security

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On Adaptive Specialisation in Genetic Improvement 19

On Adaptive Specialisation in Genetic Improvement

引用

Genetic and Evolutionary Computation Conference (GECCO)

作者： Blot, Aymeric Petke, Justyna UCL London England

ISBN: (纸本)9781450367486

Genetic improvement uses automated search to find improved versions of existing software. Software can either be evolved with general-purpose intentions or with a focus on a specific application (e.g., to improve it's efficiency for a particular class of problems). Unfortunately, software specialisation to each problem application is generally performed independently, fragmenting and slowing down an already very time-consuming search process. We propose to incorporate specialisation as an online mechanism of the general search process, in an attempt to automatically devise application classes, by benefiting from past execution history.

关键词： Genetic Improvement Search-Based Software Engineering Software Specialisation algorithm selection algorithm Configuration

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Construction of Verifier Combinations Based on Off-the-Shelf Verifiers 25th

Construction of Verifier Combinations Based on Off-the-Shelf...

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25th International Conference on Fundamental Approaches to Software Engineering (FASE) Held as Part of the Annual European Joint Conferences on Theory and Practice of Software (ETAPS)

作者： Beyer, Dirk Kanav, Sudeep Richter, Cedric Ludwig Maximilians Univ Munchen Munich Germany Carl von Ossietzky Univ Oldenburg Oldenburg Germany

ISBN: (纸本)9783030994297;9783030994280

Software verifiers have different strengths and weaknesses, depending on properties of the verification task. It is well-known that combinations of verifiers via portfolio and selection approaches can help to combine the strengths. In this paper, we investigate (a) how to easily compose such combinations from existing, 'off-the-shelf' verification tools without changing them and (b) how much performance improvement easy combinations can yield, regarding the effectiveness (number of solved problems) and efficiency (consumed resources). First, we contribute a method to systematically and conveniently construct verifier combinations from existing tools, using the composition framework coVERITEAM. We consider sequential portfolios, parallel portfolios, and algorithm selections. Second, we perform a large experiment on 8 883 verification tasks to show that combinations can improve the verification results without additional computational resources. All combinations are constructed from off-the-shelf verifiers, that is, we use them as published. The result of our work suggests that users of verification tools can achieve a significant improvement at a negligible cost (only configure our composition scripts).

关键词： Software verification Program analysis Cooperative verification Tool Combinations Portfolio algorithm selection CoVERITEAM

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Graves-CPA: A Graph-Attention Verifier Selector (Competition Contribution) 28th

Graves-CPA: A Graph-Attention Verifier Selector (Competition...

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28th International Conference on Tools and algorithms for the Construction and Analysis of Systems (TACAS) Held as Part of the 25th European Joint Conferences on Theory and Practice of Software (ETAPS)

作者： Leeson, Will Dwyer, Matthew B. Univ Virginia Charlottesville VA 22903 USA

ISBN: (纸本)9783030995270;9783030995263

GRAVES-CPA is a verification tool which uses algorithm selection to decide an ordering of underlying verifiers to most effectively verify a given program. GRAVES-CPA represents programs using an amalgam of traditional program graph representations and uses stateof-the-art graph neural network techniques to dynamically decide how to run a set of verification techniques. The GRAVES technique is implementation agnostic, but it's competition submission, GRAVES-CPA, is built using several CPAchecker configurations as its underlying verifiers.

关键词： Software Verification Graph Attention Networks Graph Neural Networks algorithm selection

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A Knowledge Discovery Approach to Understanding Relationships between Scheduling Problem Structure and Heuristic Performance

A Knowledge Discovery Approach to Understanding Relationship...

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3rd Conference on Learning and Intelligent Optimization

作者： Smith-Miles, Kate A. James, Ross J. W. Giffin, John W. Tu, Yiqing Monash Univ Sch Math Sci Melbourne Vic 3004 Australia Univ Canterbury Dept Management Christchurch New Zealand

ISBN: (纸本)9783642111686

Using a knowledge discovery approach, we seek insights into the relationships between problem structure and the effectiveness of scheduling heuristics. A large collection of 75,000 instances of the single machine early/tardy scheduling problem is generated, characterized by six features, and used to explore the performance of two common scheduling heuristics. The best heuristic is selected using rules from a decision tree with accuracy exceeding 97%. A self-organizing map is used to visualize the feature space and generate insights into heuristic performance. This paper argues for such a knowledge discovery approach to be applied to other optimization problems, to contribute to automation of algorithm selection as well as insightful algorithm design.

关键词： Scheduling heuristics algorithm selection self-organizing map performance prediction knowledge discovery

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Application of the automatic selection and configuration of clustering algorithms method for the Apache Spark framework 21

Application of the automatic selection and configuration of ...

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Proceedings of the 3rd International Conference on Advanced Information Science and System

作者： Vladimir Kazakovtsev Sergey Muravyov ITMO University Russia

ISBN: (纸本)9781450385862

This article proposes the MASSCAH method realization for Apache Spark clustering algorithms selection and configuration. Optimization of one of the clustering quality measures is used to configure the algorithm. In the course of this study, additional clustering quality measures were implemented that are not included in the Apache Spark framework, since at the moment only the silhouette criterion is available in the framework.

关键词： algorithm selection

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AutoClass: AutoML for Data Stream Classification

AutoClass: AutoML for Data Stream Classification

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Bahri, Maroua Georgantas, Nikolaos Inria Paris Paris France

ISBN: (纸本)9798350324457

Automated Machine Learning (autoML) is a novel topic that aims to tackle the parameter configuration issue using automatic monitoring models and comprises different machine learning tasks, such as feature selection, model selection, and hyper-parameter tuning. It makes easier use of algorithms for non-ML experts as well as ML experts by automating tasks that rely on expert domain knowledge. Nevertheless, autoML is in its infancy stage and not well explored yet in the offline and stream settings. In this paper, we propose automated Classification (auto-Class) method for automated algorithm selection and configuration for data stream classification. AutoClass consists of training an ensemble of different tuned configurations and selecting the best-performing configuration to do the prediction. We present experiments performed on a diverse set of real and artificial datasets and show how our proposed approach can outperform the performance of competitive state-of-the-art ensemble and single-based methods. © 2023 IEEE.

关键词： algorithm selection AutoML data stream hyper-parameter tuning stream classification

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Application of the automatic selection and configuration of clustering algorithms method for the Apache Spark framework

Application of the automatic selection and configuration of ...

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作者： Vladimir Kazakovtsev Sergey Muravyov ITMO University

关键词： apache spark machine learning clustering algorithm selection hyperparameter optimization multi-armed bandit reinforcement learning

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Meta Learning Recommendation System for Classification

Meta Learning Recommendation System for Classification

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作者： Clarence O. WilliaIII Air Force Institute of Technology Air University Air Education and Training Command

学位级别：硕士

A data driven approach is an emerging paradigm for the handling of analytic prob- lems. In this paradigm the mantra is to let the data speak freely. However, when using machine learning algorithms, the data does not naturally reveal the best or even a good approach for algorithm choice. One method to let the algorithm reveal itself is through the use of Meta Learning, which uses the features of a dataset to determine a useful model to represent the entire dataset. This research proposes an improve- ment on the meta-model recommendation system by adding classification problems to the candidate problem space with appropriate evaluation metrics for these additional problems. This research predicts the relative performance of six machine learning algorithms using support vector regression with a radial basis function as the meta learner. Six sets of data of various complexity are explored using this recommendation system and at its best, the system recommends the best algorithm 67% of the time and a "good" algorithm from 67% to 100% of the time depending on how "good" is defined.

关键词： Meta Learning Meta Modeling Recommendation System algorithm selection

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