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datafold: data-driven models for point clouds and time series on manifolds
2020
Journal of Open Source Software
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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
2021
Requirements Engineering: Foundation for Software Quality
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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
2014
2014 47th Hawaii International Conference on System Sciences
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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
2022
Maǧallaẗ al-abḥāṯ al-handasiyyaẗ
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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
2014
International Journal of Approximate Reasoning
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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
2021
Ìnformacìjnì Tehnologì ì Zasobi Navčannâ
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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]
2019
arXiv
pre-print
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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
2021
Procedia Computer Science
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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
2022
Scientific Programming
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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
2016
International Journal of Hybrid Information Technology
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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
2021
Mathematical Problems in Engineering
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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]
2021
arXiv
pre-print
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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]
2020
arXiv
pre-print
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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]
2009
Lecture Notes in Computer Science
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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
2018
IEEE Internet of Things Journal
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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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