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1Siren - Interactive and visual redescription mining — Siren - Interactive Redescription Mining
http://cs.uef.fi/siren/main/index.html
3 years ago by @becker
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siren
subgroup
clired
python
data
py
interactive
redescription
geo
mining
geospatial
redescriptions
spatial
sirensubgroupcliredpythondatapyinteractiveredescriptiongeomininggeospatialredescriptionsspatial
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1Introduction to Redescription Mining - A Tutorial
http://siren.mpi-inf.mpg.de/tutorial_sdm2017/main/RMTutorialSDM17_slides.pdf
3 years ago by @becker
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slides
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slidessubgroupsubgroupsexceptionalminingemmpatterntutorialmodelredescription
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1The Power of Saying “I don’t know”—An Introduction to Subgroup Discovery and Local Modeling | realKD
Nice illustration why local models may make sense to pay attention to.
3 years ago by @becker
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explainsubgroupsubgroupsminingillustrationpatterndiscoveryexplainableexampleexplanationuncertaintylocal
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2CORNELL CS4780 "Machine Learning for Intelligent Systems" - YouTube
https://www.youtube.com/playlist?list=PLl8OlHZGYOQ7bkVbuRthEsaLr7bONzbXS
3 years ago by @becker
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science
mining
boosting
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kilian
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weinberger
scienceminingboostingdatamachinekiliancourselecturelearningweinberger
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119: Boosting
https://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/lecturenote19.html
3 years ago by @becker
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boosting
data
machine
kilian
course
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weinberger
scienceminingboostingdatamachinekiliancourselecturelearningweinberger
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1Large scale correlation mining: fundamental performance limits
http://web.eecs.umich.edu/~hero/Preprints/hero_duke_SAHD15.pdf
4 years ago by @becker
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estimation
statistical
mining
correlation
test
paper:fastcor
estimationstatisticalminingcorrelationtestpaper:fastcor
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2Statistical Significance Tests for Comparing Machine Learning Algorithms
https://machinelearningmastery.com/statistical-significance-tests-for-comparing-machine-learning-algorithms/
4 years ago by @becker
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comparison
statistical
test
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comparisonstatisticaltestdatalearningsciencesignificanceminingcrossmachinetestinghypothesisvalidation
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1arvkevi/kneed: Knee point detection in Python
https://github.com/arvkevi/kneed
4 years ago by @becker
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elbow
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1machine learning - Getting a low ROC AUC score but a high accuracy - Stack Overflow
https://stackoverflow.com/questions/47104129/getting-a-low-roc-auc-score-but-a-high-accuracy/47111246#47111246
4 years ago by @becker
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acc
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acccomparisonrocprecisiondataaccuracylearningmetricaucscienceminingrecallevaluationmachinemetricsml
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1Mining Knowledge Graphs from Text | A Tutorial
A Tutorial
5 years ago by @nosebrain
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knowlegdegraphsminingtext
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1CART-based approach to discover emerging patterns in microarray data | Bioinformatics | Oxford Academic
https://academic.oup.com/bioinformatics/article/19/18/2465/194662
5 years ago by @becker
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fisher
asdsubgroupminingtestfischerspatterndiscoveryexactfisher
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1Algorithms | Feature Selection @ ASU
http://featureselection.asu.edu/algorithms.php
6 years ago by @becker
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learn
python
data
py3
learning
sklearn
python3
selection
mining
feature
machine
scikit
ml
learnpythondatapy3learningsklearnpython3selectionminingfeaturemachinescikitml
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1bootstrap - what is the effect of bootstap resampling in bagging algorithm(ensemble learning)? - Cross Validated
https://stats.stackexchange.com/questions/130662/what-is-the-effect-of-bootstap-resampling-in-bagging-algorithmensemble-learning
6 years ago by @becker
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without
equivalent
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data
dm
bootstrap
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resampling
bootstrapping
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mining
effect
machine
wo
replacement
ml
withoutequivalentboostingbaggingdatadmbootstraplearningresamplingbootstrappingwithminingeffectmachineworeplacementml
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1Translation and scaling invariance in regression models
https://roamanalytics.com/2016/11/17/translation-and-scaling-invariance-in-regression-models/#Scaling-affects-regularized-regression
6 years ago by @becker
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linear
scaling
mining
center
data
regression
machine
regularization
learning
centering
linearscalingminingcenterdataregressionmachineregularizationlearningcentering
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2Neue Tools sollen Qualität von Online-Texten analysieren | iX
https://www.heise.de/ix/meldung/Neue-Tools-sollen-Qualitaet-von-Online-Texten-analysieren-3582332.html#mobile_detect_force_desktop
8 years ago by @hotho
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news
dii
argument
kallimachos
crowd
mining
datasets
fake
worker
newsdiiargumentkallimachoscrowdminingdatasetsfakeworker
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1SpiegelMining: Think big. Die Artikellandschaft der letzten zwei Jahre zum selberforschen [D. Kriesel]
http://www.dkriesel.com/blog/2016/1028_spiegelmining_think_big._die_artikellandschaft_der_letzten_zwei_jahre_zum_selberforschen#the__comments
8 years ago by @hotho
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spiegelmining
mining
spiegel
text
artikellandschaft
analysis
media
spiegelminingminingspiegeltextartikellandschaftanalysismedia
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1ACL Anthology » W16
http://aclweb.org/anthology/W/W16/#2800
8 years ago by @hotho
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argument
mining
2016
proceedings
argumentmining2016proceedings
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1PhD Position: Argumentation Mining with Deep Learning and Random Forest | Ashwin Ittoo | Pulse | LinkedIn
Argumentation Mining with Deep Learning and Random Forest
8 years ago by @hotho
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rf
mining
deep
agrument
learning
am
rfminingdeepagrumentlearningam
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2The Unknown Perils of Mining Wikipedia - Lateral
https://blog.lateral.io/2015/06/the-unknown-perils-of-mining-wikipedia/
9 years ago by @thoni
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perils
mining
wikipedia
perilsminingwikipedia
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1Twitter Analytical Tools | Centre for Social Technologies
Some of the tools listed below are discussed in detail in the "Seven Layers of Social Media Analytics" book. Applications (no programming skills required) DiscoverText: http://www.screencast.com/t/VKLqaekPz Demographics Pro: http://www.demographicspro.com/ NodeXL: https://nodexl.codeplex.com/ Twitonomy: http://www.twitonomy.com/ RapidMiner: http://docs.rapidminer.com/studio/how-to/cloud-connectivity/twitter.html KPI6: https://www.kpi6.com/ Talend: http://www.datalytyx.com/twitter-sentiment-analysis-using-talend/ KNIME: https://www.knime.org/blog/knime-twitter-nodes Pentaho: http://www.patlaf.com/query-twitter-api-with-pentaho-pdi-kettle/ iScience Maps: http://maps.iscience.deusto.es/ Nexalogy: https://nexalogy.com/ Tools/Scripts/Modules (require programming skills) Twarc: https://github.com/edsu/twarc Twitter for…
9 years ago by @hotho
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nlp
twitter
mining
data
analysis
tools
extraction
nlptwitterminingdataanalysistoolsextraction
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1Hidden stratification causes clinically meaningful failures in machine learning for medical imaging
L. Oakden-Rayner, J. Dunnmon, G. Carneiro, and C. Ré. Proceedings of the ACM conference on health, inference, and learning, page 151--159. (2020)
a year ago by @becker
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subgroup
mining
failure
stratification
hidden
machine
pattern
learning
citedby:scholar:count:243
citedby:scholar:timestamp:2023-5-31
subgroupminingfailurestratificationhiddenmachinepatternlearningcitedby:scholar:count:243citedby:scholar:timestamp:2023-5-31
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2Uni-and multivariate probability density models for numeric subgroup discovery
M. Meeng, H. de Vries, P. Flach, S. Nijssen, and A. Knobbe. Intelligent Data Analysis, 24 (6): 1403--1439 (2020)
2 years ago by @becker
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subgroup
exceptional
mining
probability
citedby:scholar:count:5
numeric
discovery
multivariate
density
model
target
citedby:scholar:timestamp:2022-5-24
subgroupexceptionalminingprobabilitycitedby:scholar:count:5numericdiscoverymultivariatedensitymodeltargetcitedby:scholar:timestamp:2022-5-24
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2Drum: End-to-end differentiable rule mining on knowledge graphs
A. Sadeghian, M. Armandpour, P. Ding, and D. Wang. Advances in Neural Information Processing Systems, (2019)
2 years ago by @becker
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citedby:scholar:count:73
mining
drum
endtoend
citedby:scholar:timestamp:2022-5-11
knowledge
graph
rule
citedby:scholar:count:73miningdrumendtoendcitedby:scholar:timestamp:2022-5-11knowledgegraphrule
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1Integrated intra-and intercellular signaling knowledge for multicellular omics analysis
D. Türei, A. Valdeolivas, L. Gul, N. Palacio-Escat, M. Klein, O. Ivanova, M. Ölbei, A. Gábor, F. Theis, D. Módos and 1 other author(s). Molecular systems biology, 17 (3): e9923 (2021)
2 years ago by @becker
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immune
project:iti-single-cell
citedby:scholar:count:35
network
networks
cell
citedby:scholar:timestamp:2022-2-23
cells
system
database
pubmed
mining
background
text
cytokines
knowledge
immuneproject:iti-single-cellcitedby:scholar:count:35networknetworkscellcitedby:scholar:timestamp:2022-2-23cellssystemdatabasepubmedminingbackgroundtextcytokinesknowledge
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1Immune-centric network of cytokines and cells in disease context identified by computational mining of PubMed
K. Kveler, E. Starosvetsky, A. Ziv-Kenet, Y. Kalugny, Y. Gorelik, G. Shalev-Malul, N. Aizenbud-Reshef, T. Dubovik, M. Briller, J. Campbell and 1 other author(s). Nature biotechnology, 36 (7): 651--659 (2018)
2 years ago by @becker
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citedby:scholar:count:51
immune
project:iti-single-cell
network
networks
cell
citedby:scholar:timestamp:2022-2-23
cells
system
database
pubmed
mining
background
text
cytokines
knowledge
citedby:scholar:count:51immuneproject:iti-single-cellnetworknetworkscellcitedby:scholar:timestamp:2022-2-23cellssystemdatabasepubmedminingbackgroundtextcytokinesknowledge
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1Mining sequences with exceptional transition behaviour of varying order using quality measures based on information-theoretic scoring functions
R. Schouten, M. Bueno, W. Duivesteijn, and M. Pechenizkiy. Data Mining and Knowledge Discovery, (2021)
3 years ago by @becker
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wouter
subgroup
exceptional
emm
discovery
sequences
transition
sequence
mining
model
order
higher
markov
behavior
woutersubgroupexceptionalemmdiscoverysequencestransitionsequenceminingmodelorderhighermarkovbehavior
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1Evolutionary fuzzy rule induction process for subgroup discovery: a case study in marketing
M. Del Jesus, P. González, F. Herrera, and M. Mesonero. IEEE Transactions on Fuzzy Systems, 15 (4): 578--592 (2007)
3 years ago by @becker
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project:bmbf
subgroup
mining
discovery
citedby:scholar:count:141
citedby:scholar:timestamp:2021-7-25
fuzzy
rule
project:bmbfsubgroupminingdiscoverycitedby:scholar:count:141citedby:scholar:timestamp:2021-7-25fuzzyrule
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1Contrast set mining for distinguishing between similar diseases
P. Kralj, N. Lavrac, D. Gamberger, and A. Krstacić. Conference on Artificial Intelligence in Medicine in Europe, page 109--118. Springer, (2007)
3 years ago by @becker
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set
project:bmbf
citedby:scholar:count:32
contrast
mining
citedby:scholar:timestamp:2021-7-25
setproject:bmbfcitedby:scholar:count:32contrastminingcitedby:scholar:timestamp:2021-7-25
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3Anytime subgroup discovery in numerical domains with guarantees
A. Belfodil, A. Belfodil, and M. Kaytoue. Joint European Conference on Machine Learning and Knowledge Discovery in Databases, page 500--516. Springer, (2018)
3 years ago by @becker
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subgroup
subgroups
efficient
exceptional
mining
emm
progress
iterative
citedby:scholar:count:15
model
algorithm
citedby:scholar:timestamp:2021-7-7subgroupsubgroupsefficientexceptionalminingemmprogressiterativecitedby:scholar:count:15modelalgorithm
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2Mining Formal Concepts using Implications between Items
A. Belfodil, A. Belfodil, and M. Kaytoue. International Conference on Formal Concept Analysis, page 173--190. Springer, (2019)
3 years ago by @becker
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formal
subgroup
efficient
exceptional
mining
emm
concept
discovery
model
closure
algorithm
formalsubgroupefficientexceptionalminingemmconceptdiscoverymodelclosurealgorithm
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2Fssd-a fast and efficient algorithm for subgroup set discovery
A. Belfodil, A. Belfodil, A. Bendimerad, P. Lamarre, C. Robardet, M. Kaytoue, and M. Plantevit. 2019 IEEE International Conference on Data Science and Advanced Analytics (DSAA), page 91--99. IEEE, (2019)
3 years ago by @becker
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set
subgroup
subgroups
fast
exceptional
mining
emm
discovery
citedby:scholar:count:8
model
citedby:scholar:timestamp:2021-7-7setsubgroupsubgroupsfastexceptionalminingemmdiscoverycitedby:scholar:count:8model
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3Identifying exceptional (dis) agreement between groups
A. Belfodil, S. Cazalens, P. Lamarre, and M. Plantevit. Data Mining and Knowledge Discovery, 34 (2): 394--442 (2020)
3 years ago by @becker
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citedby:scholar:timestamp:2021-7-7
disagreement
citedby:scholar:count:1
within
exceptional
mining
emm
model
groups
citedby:scholar:timestamp:2021-7-7disagreementcitedby:scholar:count:1withinexceptionalminingemmmodelgroups
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2DEvIANT: Discovering significant exceptional (dis-) agreement within groups
A. Belfodil, W. Duivesteijn, M. Plantevit, S. Cazalens, and P. Lamarre. LIRIS UMR CNRS 5205, (2019)
3 years ago by @becker
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citedby:scholar:timestamp:2021-7-7
disagreement
within
citedby:scholar:count:2
exceptional
mining
emm
model
groups
citedby:scholar:timestamp:2021-7-7disagreementwithincitedby:scholar:count:2exceptionalminingemmmodelgroups
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2Redescription Model Mining
F. Stamm, M. Becker, M. Strohmaier, and F. Lemmerich. Proceedings of the 27nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, (2021)
3 years ago by @becker
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rmm
project:bmbf
myown
project:2023-tracking
subgroup
subgroups
exceptional
mining
emm
kdd
model
redescription
rmmproject:bmbfmyownproject:2023-trackingsubgroupsubgroupsexceptionalminingemmkddmodelredescription
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1Integrated trajectories of the maternal metabolome, proteome, and immunome predict labor onset
I. Stelzer, M. Ghaemi, X. Han, K. Ando, J. Hedou, D. Feyaerts, L. Peterson, K. Rumer, E. Tsai, E. Ganio and 28 other author(s). Science Translational Medicine, (2021)
3 years ago by @becker
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labor
project:bmbf
myown
mining
of
pregnancy
data
paper:cordis
machine
multiomics
learning
onset
laborproject:bmbfmyownminingofpregnancydatapaper:cordismachinemultiomicslearningonset
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2BoostEMM-Transparent Boosting using Exceptional Model Mining.
S. van der Zon, O. Zeev-Ben-Mordehai, T. Vrijdag, W. van Ipenburg, W. Duivesteijn, J. Veldsink, and M. Pechenizkiy. MIDAS@ PKDD/ECML, page 5--16. (2017)
3 years ago by @becker
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subgroup
exceptional
mining
emm
boosting
discovery
prediction
model
boost
classification
subgroupexceptionalminingemmboostingdiscoverypredictionmodelboostclassification
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3For real: a thorough look at numeric attributes in subgroup discovery
M. Meeng, and A. Knobbe. Data Mining and Knowledge Discovery, 35 (1): 158--212 (2021)
3 years ago by @becker
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subgroup
citedby:scholar:timestamp:2021-2-23
citedby:scholar:count:2
exceptional
mining
emm
numeric
discovery
model
attributes
attribute
subgroupcitedby:scholar:timestamp:2021-2-23citedby:scholar:count:2exceptionalminingemmnumericdiscoverymodelattributesattribute
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2Sequential Exceptional Pattern Discovery Using Pattern-Growth: An Extensible Framework for Interpretable Machine Learning on Sequential Data
D. Mollenhauer, and M. Atzmueller. (2020)
4 years ago by @becker
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reduce
exceptional
emm
prefix
sequential
gp
distributed
discovery
sequence
mining
parallel
spark
pattern
mapreduce
map
growth
model
span
reduceexceptionalemmprefixsequentialgpdistributeddiscoverysequenceminingparallelsparkpatternmapreducemapgrowthmodelspan
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115 Large-scale correlation mining for biomolecular network discovery
A. Hero, and B. Rajaratnam. Big Data Over Networks, (2016)
4 years ago by @becker
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statistical
large
test
discovery
scale
citedby:scholar:count:9
network
paper:fastcor
estimation
mining
citedby:scholar:timestamp:2021-1-7
correlation
biomolecular
statisticallargetestdiscoveryscalecitedby:scholar:count:9networkpaper:fastcorestimationminingcitedby:scholar:timestamp:2021-1-7correlationbiomolecular
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1On estimating model accuracy with repeated cross-validation
G. Vanwinckelen, and H. Blockeel. Benelearn 2012: Proceedings of the 21st belgian-dutch conference on machine learning, page 39--44. (2012)
4 years ago by @becker
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comparison
statistical
data
test
learning
science
significance
mining
cross
machine
testing
hypothesis
validation
comparisonstatisticaldatatestlearningsciencesignificanceminingcrossmachinetestinghypothesisvalidation
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