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datafold: data-driven models for point clouds and time series on manifolds

Daniel Lehmberg, Felix Dietrich, Gerta Köster, Hans-Joachim Bungartz
2020 Journal of Open Source Software  
with a design that reflects a workflow hierarchy: from lowlevel data structures and algorithms to high-level meta-models intended to solve complex machine learning tasks.  ...  An intrinsic geometry is what most data-driven models assume implicitly or explicitly in the available data, and successful machine learning algorithms adapt to this underlying structure for tasks like  ...  An intrinsic geometry is what most data-driven models assume implicitly or explicitly in the available data, and successful machine learning algorithms adapt to this underlying structure for tasks like  ... 
doi:10.21105/joss.02283 fatcat:orm4nlvgwjgxjl2m4ibrzb62fq

Requirements Engineering for Actors-with-Learning: Incorporating the Two Kinds of Models and Modeling for Full-Cognitive-Cycle RE

Eric Yu
2021 Requirements Engineering: Foundation for Software Quality  
In this short paper, we first suggest that developing machine learning applications, like other software initiatives, can benefit from familiar requirements engineering (RE) techniques such as goal-and  ...  Models are also central to recent approaches to AI, being the output of computationally intensive machine learning algorithms drawing on large datasets.  ...  Constructive comments from anonymous reviewers and from S. Nalchigar, Z. Babar, and A. Lapouchnian are gratefully acknowledged.  ... 
dblp:conf/refsq/Yu21 fatcat:n6apmbnkvnhcpllxfftlqe6bna

Towards Model-Driven Engineering for Big Data Analytics -- An Exploratory Analysis of Domain-Specific Languages for Machine Learning

Dominic Breuker
2014 2014 47th Hawaii International Conference on System Sciences  
To explore the opportunities of model-driven big data analytics, I review the main modeling languages used in machine learning as well as inference algorithms and corresponding software implementations  ...  They come along with increased supply of data scientist labor, the demand of which cannot be fulfilled at the moment.  ...  A Model-driven Critique From the perspective of model-driven engineering, graphical models constitute an interesting development in the field of machine learning.  ... 
doi:10.1109/hicss.2014.101 dblp:conf/hicss/Breuker14 fatcat:hx5h23a7enawvl5lnoacesjtvu

Task Allocation in Distributed Agile Software Development Environment Using Unsupervised Learning

Madan Singh, Department of Computer Engineering, J.C. Bose University of Science & Technology, YMCA, Faridabad, Haryana (India), Naresh Chauhan, Rashmi Popli, Department of Computer Engineering, J.C. Bose University of Science & Technology, YMCA, Faridabad, Haryana (India), Department of Computer Engineering, J.C. Bose University of Science & Technology, YMCA, Faridabad, Haryana (India)
2022 Maǧallaẗ al-abḥāṯ al-handasiyyaẗ  
For applying classification and rank ordering on data set of employees, Meta - Classifier Based Prediction Model (MCBPM) has been used that applied unsupervised learning.  ...  For i  1 to M do Construct a new data set D' End for Step 6:-Learn a second level classifier. Learn a new meta classifier h' based on newly constructed data set D'.  ...  Unsupervised learning [9], is machine learning mechanism where models are trained using un-labelled and uncategorized data.  ... 
doi:10.36909/jer.icmet.17167 fatcat:e7perrbvpjgnblqi6bm7f2teju

Bayesian network approach to multinomial parameter learning using data and expert judgments

Yun Zhou, Norman Fenton, Martin Neil
2014 International Journal of Approximate Reasoning  
Empirically, the new method achieves much greater learning accuracy (compared to both state-of-the-art machine learning techniques and directly competing methods) with much less data.  ...  Even with a fixed predefined model structure and very large amounts of relevant data, machine learning methods do not consistently achieve great accuracy compared to the ground truth when learning the  ...  Empirically, the new method achieves much greater learning accuracy (compared to both state-of-the-art machine learning techniques and directly competing methods) with much less data.  ... 
doi:10.1016/j.ijar.2014.02.008 fatcat:hwx4k66l5facrdkxy7ax3obtka

STATISTICAL SOFTWARE R IN CORPUS-DRIVEN RESEARCH AND MACHINE LEARNING

Viktoriia V. Zhukovska, Oleksandr O. Mosiiuk
2021 Ìnformacìjnì Tehnologì ì Zasobi Navčannâ  
classifiers in machine learning.  ...  The analyzed linguistic data are employed to build a machine model for the classification of the given constructions.  ...  Obviously, further studies incorporating methods and tools of machine learning based on the statistical software complex R into corpus-driven linguistics will be of considerable interest.  ... 
doi:10.33407/itlt.v86i6.4627 fatcat:hlh5kb5555gljovkrpow6mw4ou

Data Science and Digital Systems: The 3Ds of Machine Learning Systems Design [article]

Neil D. Lawrence
2019 arXiv   pre-print
Machine learning solutions, in particular those based on deep learning methods, form an underpinning of the current revolution in "artificial intelligence" that has dominated popular press headlines and  ...  Here we give an overview of the 3Ds of ML systems design: Data, Design and Deployment. By considering the 3Ds we can move towards data first design.  ...  The technology that has driven the revolution in AI is machine learning.  ... 
arXiv:1903.11241v1 fatcat:hotg2pksj5h6jpqa2b3b4aulom

Towards Data-Driven Reliability Modeling for Cyber-Physical Production Systems

Jonas Friederich, Sanja Lazarova-Molnar
2021 Procedia Computer Science  
We, furthermore, introduce a case study that will aid the development and testing of the proposed novel data-driven approach.  ...  We, furthermore, introduce a case study that will aid the development and testing of the proposed novel data-driven approach.  ...  This case study will be used to develop and test methodologies that contribute to the overall vision of data-driven reliability modeling.  ... 
doi:10.1016/j.procs.2021.03.073 fatcat:vbxw4gsogfajdaffbms5s63sxi

Construction and Application of a Data-Driven Abstract Extraction Model for English Text

Hui Peng, Sheng Bin
2022 Scientific Programming  
In this paper, a single English text is taken as the research object, and the automatic extraction method of text summary is studied using data-driven method.  ...  Comparative experiments were conducted on several datasets and small uniformly distributed private datasets were constructed.  ...  Firstly, the dependency syntax analysis is carried out on the text according to the dependency syntax analysis theory, and the text graph model is constructed with sentences as nodes in combination with  ... 
doi:10.1155/2022/9497783 fatcat:drik52dsszbcpdornuc7dtxyre

A Survey of Quality Prediction Methods of Service-oriented Systems

Jinyu Kai, Huaikou Miao, Honghao Gao
2016 International Journal of Hybrid Information Technology  
Machine Learning Prediction Approaches Machine learning prediction approaches construct predict model based on machine learning techniques.  ...  This class of approaches leverages data mining and machine learning capacities to train prediction models using historic monitoring data.  ... 
doi:10.14257/ijhit.2016.9.4.17 fatcat:outswsgzfzeuvdx46rhqo6oxpa

A Review of Data-Driven Short-Term Voltage Stability Assessment of Power Systems: Concept, Principle, and Challenges

Jiting Cao, Meng Zhang, Yang Li, Jun Peng
2021 Mathematical Problems in Engineering  
This article comprehensively sorts out the STVS problems of power systems from the perspective of data-driven methods and discusses existing challenges.  ...  In particular, in modern power grids, the proportion of dynamic loads with fast recovery characteristics such as air conditioners, refrigerators, and industrial motors is increasing.  ...  Data-Driven STVSA Based on Shallow Machine Learning. e applications of data-driven STVSA field were initially mainly reflected in the widespread use of shallow machine learning.  ... 
doi:10.1155/2021/5920244 fatcat:bqa2hfkej5bhhflyqoan2ake3m

A critical review of data-driven transient stability assessment of power systems: principles, prospects and challenges [article]

Shitu Zhang, Zhixun Zhu, Yang Li
2021 arXiv   pre-print
Since traditional time-domain simulations and direct method cannot meet the actual operation requirements of power systems, data-driven TSA has attracted growing attention from both academia and industry  ...  This paper makes a comprehensive review from the following four aspects: feature extraction and selection, model construction, online learning and rule extraction; and then, summarizes the challenges and  ...  Existing model construction methods of data-driven TSA mainly include the following categories: ANN, SVM, ensemble learning (EL), and deep learning (DL).  ... 
arXiv:2111.00978v1 fatcat:byrrmsopbfdnxjghsw4vn7p4im

Learning Concise Models from Long Execution Traces [article]

Natasha Yogananda Jeppu, Tom Melham, Daniel Kroening, John O'Leary
2020 arXiv   pre-print
We describe a new algorithm for automatically extracting useful models, as automata, from execution traces of a HW/SW system driven by software exercising a use-case of interest.  ...  We learn concise models capturing transaction-level, system-wide behaviour--experimentally demonstrating the approach using traces from a variety of sources, including the x86 QEMU virtual platform and  ...  We thank Daniel Bristot for his help with the RT Linux Kernel.  ... 
arXiv:2001.05230v3 fatcat:m3sfunqbobcdvhmb3rgieyfk7i

Model-Driven Engineering Meets Generic Language Technology [chapter]

M. G. J. van den Brand
2009 Lecture Notes in Computer Science  
One of the key points of model-driven engineering is raising the level of abstraction in software development. This phenomenon is not new.  ...  In the sixties of the previous century, the first high-level programming languages were developed and they also increased the abstraction level of software development.  ...  -There is a huge amount of compiler construction related research from which the model-driven software engineering can learn.  ... 
doi:10.1007/978-3-642-00434-6_2 fatcat:e6szywrhqveh7fqm33nm6cmyke

Guest Editorial: Special Issue on AI Powered Network Management: Data-Driven Approaches Under Resource Constraints

Shuguang Cui, Liuqing Yang, Xiang Cheng
2018 IEEE Internet of Things Journal  
data-driven approaches as well as applications.  ...  With the fuel (IoT data) and the engine (AI), data-driven network management will enable us to dynamically and adaptively meet the spatio-temporal network demands in the most resource-aware and resource-smart  ...  in the paper entitled "Data Driven Feature Selection for Machine Learning Algorithms in Computer Vision."  ... 
doi:10.1109/jiot.2018.2887323 fatcat:ige7tqb3o5f6lkrcd7e5ulqd7m
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