A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
Filters
Towards Geo-Distributed Machine Learning
[article]
2016
arXiv
pre-print
Preserved Fulltext
These types of applications form a new class of learning problems, which we call Geo-Distributed Machine Learning (GDML). ...
The resulting data is "born" geographically distributed. On the other hand, many machine learning applications require a global view of such data in order to achieve the best results. ...
These types of applications that deal with geo-distributed datasets belong to a new class of learning problems, which we call Geo-Distributed Machine Learning (GDML). ...
arXiv:1603.09035v1
fatcat:skmpf6odwzemdick6oncj6wnoe
Open Source Science for Large-Scale Data Mining in Earth Observation
2022
Zenodo
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
The presentation shares to the NASA Earth System Observatory team DLR's expertise on the design of open-source-based big geo-spatial data processing platforms for remote sensing AI workloads in the context ...
■ efficient data assembly of co-registered spatio-temporal geo-information for machine learning ■ co-location of data and compute for Big Geo-Data processing ■ geo-data discovery and cross-layer operations ...
Large-Scale Data Mining in Earth Observation*○ a key challenge for deep/machine learning in Earth observation is limited availability of labels for model training due to the human labor-intensive collection ...
doi:10.5281/zenodo.6315121
fatcat:mxo2pc23jzfldkahu5oz4xnx2m
Towards Scalable Geospatial Remote Sensing for Efficient OSM Labeling
2019
Zenodo
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Towards Scalable Geospatial Remote Sensing for Efficient OSM Labeling In: Minghini, M., Grinberger, A.Y., Juhász, L., Yeboah, G., Mooney, P. (Eds.). ...
Our work discusses and demonstrates how to link tools from big data analytics and machine learning to geo-spatial datasets at scale in order to extract value from openly available spatio-temporal datasets ...
For example, projects for distributed non-relational database systems such as HBase ( https://hbase.apache.org/ ) or in-memory distributed compute frameworks such as Spark are available to run on commodity ...
doi:10.5281/zenodo.3387714
fatcat:zag2m6lmnfdxtjigq6baqnlv6a
ALE: automated label extraction from GEO metadata
2017
BMC Bioinformatics
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
Conclusion: Here we present an automated method to extract labels for age, gender, and tissue from textual metadata and GEO data using both a heuristic approach as well as machine learning. ...
We then use machine-learning methods to predict labels, based upon gene expression of the samples and compare this to the text-based method. ...
This choice allows for more certainty in the results, which are used as training data provided to the GEO expression machine learning algorithm. ...
doi:10.1186/s12859-017-1888-1
pmid:29297276
pmcid:PMC5751806
fatcat:s5oihdfauzeclfa6ebpm2a2mnq
A Survey on Machine Learning for Geo-Distributed Cloud Data Center Management
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
data-driven and Machine Learning (ML) based alternatives. ...
But these techniques are difficult to scale to geo-distributed problem sizes and have limited applicability in dynamic heterogeneous system environments, forcing cloud service providers to explore intelligent ...
Hogade
I
OVERVIEW OF MACHINE LEARNING METHODS Machine Learning (ML) refers to a machine's ability to learn from data. ...
arXiv:2205.08072v1
fatcat:nz3vvmdrard6hgydagnsadrnpm
2020 Index IEEE Transactions on Cloud Computing Vol. 8
2021
IEEE Transactions on Cloud Computing
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Predicting Workflow Task Execution Time in the Cloud Using A Two-Stage Machine Learning Approach. Pham, T., +, TCC Jan. ...
Revenue Maximization for Dynamic Expansion of Geo-Distributed Cloud Data Centers. ...
Frequency control Performance-Based Pricing in Multi-Core Geo-Distributed Cloud Computing. Lucanin, D., +, TCC Oct.-Dec. 2020 ...
doi:10.1109/tcc.2021.3055041
fatcat:nppinqsievad3gr42ppehwp7yi
Using Data-Mining Technique for Census Analysis to Give GeoSpatial Distribution of Nigeria
2013
IOSR Journal of Computer Engineering
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
distribution. ...
This paper is an effort towards harnessing the power of data-mining technique to develop mining model applicable to the analysis of census data that could uncover some hidden patterns to get their geo-spatial ...
The roots of data-mining originate in three areas, Classical statistics, artificial intelligence and machine learning. ...
doi:10.9790/0661-1420105
fatcat:2hdzige7ofgpndanx5ta637juu
Machine Learning to Predict the Global Distribution of Aerosol Mixing State Metrics
2018
Atmosphere
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
This framework demonstrates how machine learning can be applied to bridge the gap between detailed process modeling and a large-scale climate model. ...
We used the output of a large ensemble of particle-resolved box model simulations in conjunction with machine learning techniques to train a model of the mixing state metric χ. ...
Figure 7 . 7 Global distribution of size-resolved χ values from the machine-learning model based GEOS-Chem-TOMAS inputs for the months of: January (top); and July (bottom). ...
doi:10.3390/atmos9010015
fatcat:p3so2rc35jbojmzajh7rxl5f5i
SEMANTIC INDEXING OF TERRASAR-X AND IN SITU DATA FOR URBAN ANALYTICS
2015
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit the original URL.
The file type is application/pdf.
Image processing together with machine learning methods, relevance feedback techniques, and human expertise are used to annotate the image content into a land use land cover catalogue. ...
All the generated information is stored into a geo-database supporting the link between different types of information and the computation of queries and analytics. ...
Semantic definition via machine learning methods: When all the generated information is available in the geo-database a new process starts: the semantic definition by using the support vector machine and ...
doi:10.5194/isprsarchives-xl-1-w5-185-2015
fatcat:egonz6z53bdx3ldhasy64ea3ku
Predicting Loss Risks for B2B Tendering Processes
[article]
2021
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Here we also define M (W ), where we train a machine learning model M with respect to the sample weights W . ...
CONCLUSION In this paper, we presented a novel machine learning model that performed multi-classification for win prediction. ...
arXiv:2109.06815v1
fatcat:44g62bmmqrbadc2hlcmkjshr6q
Table of Contents
2020
2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC)
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
Clouds
DisGB: Using Geo-Context Information for Efficient Routing in Geo-Distributed Pub/Sub
Systems 67
Jonathan Hasenburg (Technische Universität Berlin) and David Bermbach
(Technische Universität ...
Bittencourt (University of Campinas,
Brazil)
Robust Resource Scaling of Containerized Microservices with Probabilistic Machine Learning 122
Peng Kang (University of Texas at San Antonio) and Palden ...
doi:10.1109/ucc48980.2020.00004
fatcat:4iq4eh53nnb65f3whbobxcemiq
Wikiwhere: An interactive tool for studying the geographical provenance of Wikipedia references
[article]
2016
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
Instead of relying solely on the IP location of a given URL our machine learning models consider several features. ...
Such patterns could be an indicator of bias towards certain national contexts when referencing facts and statements in Wikipedia. ...
To investigate this suspicion, we set up a machine learning model to infer geoprovenance. ...
arXiv:1612.00985v2
fatcat:efci33enbvgevpdv7w6syhlkz4
Bayesian Selection Of Grammar Productions For The Language Of Thought
[article]
2017
bioRxiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2018; you can also visit the original URL.
The file type is application/pdf.
Probabilistic proposals of Language of Thoughts (LoTs) can explain learning across different domains as statistical inference over a compositionally structured hypothesis space. ...
This constitutes a first step towards a formal link between probability and complexity modeling frameworks for LoTs. ...
Testing the Coding Theorem for Geo Despite the fact that P M and K M are defined over a Turing Machine M, the reader should note that a LoT is not usually formalized with a Turing Machine, but instead ...
doi:10.1101/141358
fatcat:ty4rfls7ancgpb4jgl22dcibba
FedILC: Weighted Geometric Mean and Invariant Gradient Covariance for Federated Learning on Non-IID Data
[article]
2022
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
Federated learning is a distributed machine learning approach which enables a shared server model to learn by aggregating the locally-computed parameter updates with the training data from spatially-distributed ...
distribution is non-i.i.d. with respect to the training domains. ...
Introduction Federated learning is a machine learning paradigm where the local clients collaboratively train a model under the orchestration of a central server (1; 2; 3). ...
arXiv:2205.09305v1
fatcat:g5luxwxewrf7heodzm3jkj2lzm
Toward interactive search in remote sensing imagery
2010
Cyber Security, Situation Management, and Impact Assessment II; and Visual Analytics for Homeland Defense and Security II
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
By contrast, the vast majority of algorithms developed in machine learning aim to replace human users in data exploitation. ...
In this paper we describe a recently introduced machine learning problem, called rare category detection, which may be a better match to visual analytic environments. ...
BROAD-AREA SEARCH IN REMOTE SENSING MAGERY One application where interactive machine learning will be particularly useful is broad-area search of geo-spatial imagery . ...
doi:10.1117/12.850787
fatcat:gdbtc5fhiffjldoqa5ejdfyaju
« Previous
Showing results 1 — 15 out of 25,754 results