becker > regression

закладки (спрятать)15
показать
всё
только закладки
закладки на страницу
5
10
20
50
100
RSS
BibTeX
XML

1Bayesian linear regression for practitioners • Max Halford
https://maxhalford.github.io/blog/bayesian-linear-regression/
год назад , @becker
bayesian
linear
regression
explained
tutorial
theory
bayesianlinearregressionexplainedtutorialtheory
(0)
копироватьудалить
- Запись сообщества
- посмотреть историю записи
1Bayesian Linear Regression
https://gregorygundersen.com/blog/2020/02/04/bayesian-linear-regression/
2 лет назад , @becker
bayesian
linear
regression
background
knowledge
integration
bayes
bayesianlinearregressionbackgroundknowledgeintegrationbayes
(0)
копироватьудалить
- Запись сообщества
- посмотреть историю записи
1(2) Manuel Gomez Rodriguez - Learning Under Algorithmic Triage - YouTube
https://www.youtube.com/watch?v=008F-4tc0MM&ab_channel=ForschungszentrumL3S
3 лет назад , @becker
uncertainty
triage
policy
human
deferral
subgroups
subgroup
ridge
regression
optimization
uncertaintytriagepolicyhumandeferralsubgroupssubgroupridgeregressionoptimization
(0)
копироватьудалить
- Запись сообщества
- посмотреть историю записи
1Bayesian regression predictions - Tutorials - Pyro Discussion Forum
https://forum.pyro.ai/t/bayesian-regression-predictions/1115/8
5 лет назад , @becker
bayes
pyro
bayesian
regression
sample
prediction
bayespyrobayesianregressionsampleprediction
(0)
копироватьудалить
- Запись сообщества
- посмотреть историю записи
1Remove dependency on TracePosterior and deprecate utilities in AbstractInfer · Issue #1930 · pyro-ppl/pyro
https://github.com/pyro-ppl/pyro/issues/1930
5 лет назад , @becker
bayes
pyro
bayesian
regression
sample
prediction
bayespyrobayesianregressionsampleprediction
(0)
копироватьудалить
- Запись сообщества
- посмотреть историю записи

&lang;&lang;
⟨
1
2
3
&rang;
⟩⟩

публикации (спрятать)28
показать
всё
только публикации
публикации на страницу
5
10
20
50
100
расширенный...
RSS
BibTeX
RDF
дальше...

1Feature-weighted elastic net: using "features of features" for better prediction
J. Tay, N. Aghaeepour, T. Hastie, и R. Tibshirani. Statistica Sinica, 33 (1): 259--279 (января 2023)
9 месяцев назад , @becker
background
bki
ien
informed
integration
knowledge
learning
linear
machine
ml
regression
backgroundbkiieninformedintegrationknowledgelearninglinearmachinemlregression
(0)
копироватьудалитьдобавить публикацию в буфер
1Time series extrinsic regression: Predicting numeric values from time series data
C. Tan, C. Bergmeir, F. Petitjean, и G. Webb. Data Mining and Knowledge Discovery, (2021)
год назад , @becker
time
series
extrinsic
regression
citedby:scholar:count:65
citedby:scholar:timestamp:2023-11-4
timeseriesextrinsicregressioncitedby:scholar:count:65citedby:scholar:timestamp:2023-11-4
(0)
копироватьудалитьдобавить публикацию в буфер
9Regression shrinkage and selection via the lasso
R. Tibshirani. Journal of the Royal Statistical Society: Series B (Methodological), 58 (1): 267--288 (1996)
3 лет назад , @becker
um1
regression
shrinkage
lasso
citedby:scholar:count:43656
citedby:scholar:timestamp:2022-1-28
um1regressionshrinkagelassocitedby:scholar:count:43656citedby:scholar:timestamp:2022-1-28
(0)
копироватьудалитьдобавить публикацию в буфер
1Bayesian multitask learning regression for heterogeneous patient cohorts
A. Goncalves, P. Ray, B. Soper, D. Widemann, M. Nyg\aard, J. Nyg\aard, и A. Sales. Journal of Biomedical Informatics: X, (2019)
3 лет назад , @becker
bayes
bayesian
multitask
multi
task
learning
regression
heterogeneous
patient
medical
records
citedby:scholar:count:4
citedby:scholar:timestamp:2022-1-26
bayesbayesianmultitaskmultitasklearningregressionheterogeneouspatientmedicalrecordscitedby:scholar:count:4citedby:scholar:timestamp:2022-1-26
(0)
копироватьудалитьдобавить публикацию в буфер
14OpenLUR: Off-the-shelf air pollution modeling with open features and machine learning
F. Lautenschlager, M. Becker, K. Kobs, M. Steininger, P. Davidson, A. Krause, и A. Hotho. Atmospheric Environment, (2020)
5 лет назад , @becker
air
automl
bayes
eva
everyaware
land
lur
myown
open
optimization
project:bmbf
quality
regression
use
airautomlbayesevaeveryawarelandlurmyownopenoptimizationproject:bmbfqualityregressionuse
(0)
копироватьудалитьдобавить публикацию в буфер
1Principal component-guided sparse regression
J. Tay, J. Friedman, и R. Tibshirani. arXiv preprint arXiv:1810.04651, (2018)
5 лет назад , @becker
component
guided
nalab
principal
r01
regression
sparse
componentguidednalabprincipalr01regressionsparse
(0)
копироватьудалитьдобавить публикацию в буфер
4Overlapping community detection at scale: a nonnegative matrix factorization approach
J. Yang, и J. Leskovec. Proceedings of the sixth ACM international conference on Web search and data mining, стр. 587--596. ACM, (2013)
5 лет назад , @becker
community
correlation
detection
graph
matrix
nalab
network
regression
communitycorrelationdetectiongraphmatrixnalabnetworkregression
(0)
копироватьудалитьдобавить публикацию в буфер
4Variational AutoEncoder For Regression: Application to Brain Aging Analysis
Q. Zhao, E. Adeli, N. Honnorat, T. Leng, и K. Pohl. arXiv preprint arXiv:1904.05948, (2019)
5 лет назад , @becker
auto
autoencoder
conditional
cvae
encoder
regression
vae
variational
autoautoencoderconditionalcvaeencoderregressionvaevariational
(0)
копироватьудалитьдобавить публикацию в буфер
2Self-Adaptive Revised Land Use Regression Models for Estimating PM2. 5 Concentrations in Beijing, China
L. Hu, J. Liu, и Z. He. Sustainability, 8 (8): 786 (2016)
7 лет назад , @becker
p2map
eva
osm
lur
land
use
regression
open
street
map
air
quality
pollution
prediction
linear
citedby:scholar:count:4
citedby:scholar:timestamp:2018-2-21
p2mapevaosmlurlanduseregressionopenstreetmapairqualitypollutionpredictionlinearcitedby:scholar:count:4citedby:scholar:timestamp:2018-2-21
(0)
копироватьудалитьдобавить публикацию в буфер
2Land use regression models for ultrafine particles in six European areas
E. van Nunen, R. Vermeulen, M. Tsai, N. Probst-Hensch, A. Ineichen, M. Davey, M. Imboden, R. Ducret-Stich, A. Naccarati, D. Raffaele и 1 other автор(ы). Environmental science & technology, 51 (6): 3336--3345 (2017)
7 лет назад , @becker
air
citedby:scholar:count:7
citedby:scholar:timestamp:2018-2-21
eva
features
land
lur
map
open
osm
p2map
pollution
quality
regression
street
use
aircitedby:scholar:count:7citedby:scholar:timestamp:2018-2-21evafeatureslandlurmapopenosmp2mappollutionqualityregressionstreetuse
(0)
копироватьудалитьдобавить публикацию в буфер
6Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: Estimation of PM10 concentrations on a daily basis
M. Alam, и A. McNabola. Journal of the Air & Waste Management Association, 65 (5): 628-640 (2015)PMID: 25947321.
7 лет назад , @becker
air
eva
features
land
lur
map
open
osm
p2map
pollution
prediction
quality
regression
roads
street
use
airevafeatureslandlurmapopenosmp2mappollutionpredictionqualityregressionroadsstreetuse
(0)
копироватьудалитьдобавить публикацию в буфер
3ADVANCING THE USE OF MOBILE MONITORING DATA FOR AIR POLLUTION MODELLING
M. Adams. (2015)
7 лет назад , @becker
lur
land
use
regression
air
quality
pollution
neural
networks
nn
prediction
p2map
eva
lurlanduseregressionairqualitypollutionneuralnetworksnnpredictionp2mapeva
(0)
копироватьудалитьдобавить публикацию в буфер
6Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: Estimation of PM10 concentrations on a daily basis
M. Alam, и A. McNabola. Journal of the Air & Waste Management Association, 65 (5): 628--640 (2015)
7 лет назад , @becker
lur
eva
p2map
nn
pm10
neural
network
land
use
regression
air
pollution
quality
non
parametric
citedby:scholar:count:7
citedby:scholar:timestamp:2018-2-21
lurevap2mapnnpm10neuralnetworklanduseregressionairpollutionqualitynonparametriccitedby:scholar:count:7citedby:scholar:timestamp:2018-2-21
(0)
копироватьудалитьдобавить публикацию в буфер
2Mapping real-time air pollution health risk for environmental management: combining mobile and stationary air pollution monitoring with neural network models
M. Adams, и P. Kanaroglou. Journal of environmental management, (2016)
7 лет назад , @becker
lur
land
use
regression
air
pollution
quality
eva
p2map
neural
network
nn
mobile
stationary
prediction
citedby:scholar:count:31
citedby:scholar:timestamp:2018-2-21
lurlanduseregressionairpollutionqualityevap2mapneuralnetworknnmobilestationarypredictioncitedby:scholar:count:31citedby:scholar:timestamp:2018-2-21
(0)
копироватьудалитьдобавить публикацию в буфер
3A Spatiotemporal Prediction Framework for Air Pollution Based on Deep RNN
J. Fan, Q. Li, J. Hou, X. Feng, H. Karimian, и S. Lin. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, (2017)
7 лет назад , @becker
air
deep
eva
land
lur
network
neural
nn
p2map
pollution
quality
regression
temporal
use
airdeepevalandlurnetworkneuralnnp2mappollutionqualityregressiontemporaluse
(0)
копироватьудалитьдобавить публикацию в буфер
1Land Use Random Forests for Estimation of Exposure to Elemental Components of Particulate Matter
R. Brokamp. University of Cincinnati, (2016)
7 лет назад , @becker
air
dissertation
eva
forest
land
learning
lur
lurf
machine
p2map
pm
pollution
quality
random
regression
use
variance
airdissertationevaforestlandlearninglurlurfmachinep2mappmpollutionqualityrandomregressionusevariance
(0)
копироватьудалитьдобавить публикацию в буфер
3Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches
C. Brokamp, R. Jandarov, M. Rao, G. LeMasters, и P. Ryan. Atmospheric Environment, (2017)
7 лет назад , @becker
air
citedby:scholar:count:11
citedby:scholar:timestamp:2018-2-21
comparison
eva
forest
land
learning
linear
lur
machine
ml
p2map
pollution
quality
random
regression
use
aircitedby:scholar:count:11citedby:scholar:timestamp:2018-2-21comparisonevaforestlandlearninglinearlurmachinemlp2mappollutionqualityrandomregressionuse
(0)
копироватьудалитьдобавить публикацию в буфер
4Air pollution mapping using nonlinear land use regression models
A. Champendal, M. Kanevski, и P. Huguenot. International Conference on Computational Science and Its Applications, стр. 682--690. Springer, (2014)
7 лет назад , @becker
citedby:scholar:count:6
citedby:scholar:timestamp:2018-2-21
eva
forest
land
lur
network
neural
p2map
random
regression
use
citedby:scholar:count:6citedby:scholar:timestamp:2018-2-21evaforestlandlurnetworkneuralp2maprandomregressionuse
(0)
копироватьудалитьдобавить публикацию в буфер
2Extending Participatory Sensing to Personal Exposure Using Microscopic Land Use Regression Models
L. Dekoninck, D. Botteldooren, и L. Int Panis. International Journal of Environmental Research and Public Health, 14 (6): 586 (2017)
7 лет назад , @becker
p2map
land
use
regression
lur
participatory
sensing
ps
everyaware
eva
p2maplanduseregressionlurparticipatorysensingpseveryawareeva
(0)
копироватьудалитьдобавить публикацию в буфер
2Crowdsourcing spatial phenomena using trust-based heteroskedastic gaussian processes
M. Venanzi, A. Rogers, и N. Jennings. First AAAI Conference on Human Computation and Crowdsourcing, (2013)
8 лет назад , @becker
p2map
heatmap
interpolation
gauss
map
bc
regression
bayes
crowdsourcing
citedby:scholar:count:14
citedby:scholar:timestamp:2017-5-26
p2mapheatmapinterpolationgaussmapbcregressionbayescrowdsourcingcitedby:scholar:count:14citedby:scholar:timestamp:2017-5-26
(0)
копироватьудалитьдобавить публикацию в буфер
3Pre-Deployment Testing, Augmentation and Calibration of Cross-Sensitive Sensors.
B. Maag, O. Saukh, D. Hasenfratz, и L. Thiele. EWSN, стр. 169--180. (2016)
8 лет назад , @becker
airprobe
neural
networks
multi
linear
regression
calibration
sensor
air
quality
citedby:scholar:count:3
citedby:scholar:timestamp:2017-5-12
airprobeneuralnetworksmultilinearregressioncalibrationsensorairqualitycitedby:scholar:count:3citedby:scholar:timestamp:2017-5-12
(0)
копироватьудалитьдобавить публикацию в буфер
4Retrospective Higher-Order Markov Processes for User Trails
T. Wu, и D. Gleich. arXiv preprint arXiv:1704.05982, (2017)
8 лет назад , @becker
auto
autoregressive
chain
extension
higher
markov
order
process
regression
regressive
retrospective
toread
trails
autoautoregressivechainextensionhighermarkovorderprocessregressionregressiveretrospectivetoreadtrails
(0)
копироватьудалитьдобавить публикацию в буфер
3Dynamic bayesian networks: Representation, inference and learning
K. Murphy. University of California, Berkeley, (2002)
8 лет назад , @becker
ar
auto
chain
citedby:scholar:count:2586
citedby:scholar:timestamp:2017-3-30
diss
hidden
inthesis
markov
mixedtrails
regression
arautochaincitedby:scholar:count:2586citedby:scholar:timestamp:2017-3-30disshiddeninthesismarkovmixedtrailsregression
(0)
копироватьудалитьдобавить публикацию в буфер
2Exceptionally Monotone Models: The Rank Correlation Model Class for Exceptional Model Mining
L. Downar, и W. Duivesteijn. International Conference on Data Mining (ICDM), стр. 111--120. (2015)
8 лет назад , @becker
application
correlation
diss
emm
exceptional
function
imported
inthesis
kendall
mining
model
potential
prediction
quality
regression
spearman
subtrails
tau
applicationcorrelationdissemmexceptionalfunctionimportedinthesiskendallminingmodelpotentialpredictionqualityregressionspearmansubtrailstau
(0)
копироватьудалитьдобавить публикацию в буфер
1Cardiorespiratory fitness estimation using wearable sensors: Laboratory and free-living analysis of context-specific submaximal heart rates
M. Altini, P. Casale, J. Penders, G. ten Velde, G. Plasqui, и O. Amft. Journal of Applied Physiology, 120 (9): 1082--1096 (мая 2016)
8 лет назад , @becker
health
heart
linear
rate
regression
sensor
sport
svm
wearable
healthheartlinearrateregressionsensorsportsvmwearable
(0)
копироватьудалитьдобавить публикацию в буфер
2Development of land use regression models for PM2. 5, PM2. 5 absorbance, PM10 and PMcoarse in 20 European study areas; results of the ESCAPE project
M. Eeftens, R. Beelen, K. de Hoogh, T. Bellander, G. Cesaroni, M. Cirach, C. Declercq, A. Dedele, E. Dons, A. de Nazelle и 1 other автор(ы). Environmental science & technology, 46 (20): 11195--11205 (2012)
10 лет назад , @becker
air
land
lur
model
modeling
pollution
quality
regression
sitc
urban
use
airlandlurmodelmodelingpollutionqualityregressionsitcurbanuse
(0)
копироватьудалитьдобавить публикацию в буфер
2Multi-dimensional regression analysis of time-series data streams
Y. Chen, G. Dong, J. Han, B. Wah, и J. Wang. Proceedings of the 28th international conference on Very Large Data Bases, стр. 323--334. VLDB Endowment, (2002)
11 лет назад , @becker
analysis
dimensional
mining
multi
regression
s:2013-12-18_time-series-mining
series
time
analysisdimensionalminingmultiregressions:2013-12-18_time-series-miningseriestime
(0)
копироватьудалитьдобавить публикацию в буфер
1Gaussian processes for regression
C. Williams, и C. Rasmussen. Advances in Neural Information Processing Systems 8, MIT, Copyright of the Massachusetts Institute of Technology Press (MIT Press).(июня 1996)
11 лет назад , @becker
calibration
gaussian
prediction
processes
regression
s:2013-08-22_calibration
calibrationgaussianpredictionprocessesregressions:2013-08-22_calibration
(0)
копироватьудалитьдобавить публикацию в буфер

&lang;&lang;
⟨
1
&rang;
⟩⟩

BibSonomy

закладки (спрятать)15
показать
всё
только закладки
закладки на страницу
5
10
20
50
100
RSS
BibTeX
XML

1Bayesian linear regression for practitioners • Max Halford

1Bayesian Linear Regression

1(2) Manuel Gomez Rodriguez - Learning Under Algorithmic Triage - YouTube

1Bayesian regression predictions - Tutorials - Pyro Discussion Forum

1Remove dependency on TracePosterior and deprecate utilities in AbstractInfer · Issue #1930 · pyro-ppl/pyro

публикации (спрятать)28
показать
всё
только публикации
публикации на страницу
5
10
20
50
100
расширенный...
RSS
BibTeX
RDF
дальше...

1Feature-weighted elastic net: using "features of features" for better prediction

1Time series extrinsic regression: Predicting numeric values from time series data

9Regression shrinkage and selection via the lasso

1Bayesian multitask learning regression for heterogeneous patient cohorts

14OpenLUR: Off-the-shelf air pollution modeling with open features and machine learning

1Principal component-guided sparse regression

4Overlapping community detection at scale: a nonnegative matrix factorization approach

4Variational AutoEncoder For Regression: Application to Brain Aging Analysis

2Self-Adaptive Revised Land Use Regression Models for Estimating PM2. 5 Concentrations in Beijing, China

2Land use regression models for ultrafine particles in six European areas

6Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: Estimation of PM10 concentrations on a daily basis

3ADVANCING THE USE OF MOBILE MONITORING DATA FOR AIR POLLUTION MODELLING

6Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: Estimation of PM10 concentrations on a daily basis

2Mapping real-time air pollution health risk for environmental management: combining mobile and stationary air pollution monitoring with neural network models

3A Spatiotemporal Prediction Framework for Air Pollution Based on Deep RNN

1Land Use Random Forests for Estimation of Exposure to Elemental Components of Particulate Matter

3Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches

4Air pollution mapping using nonlinear land use regression models

2Extending Participatory Sensing to Personal Exposure Using Microscopic Land Use Regression Models

2Crowdsourcing spatial phenomena using trust-based heteroskedastic gaussian processes

3Pre-Deployment Testing, Augmentation and Calibration of Cross-Sensitive Sensors.

4Retrospective Higher-Order Markov Processes for User Trails

3Dynamic bayesian networks: Representation, inference and learning

2Exceptionally Monotone Models: The Rank Correlation Model Class for Exceptional Model Mining

1Cardiorespiratory fitness estimation using wearable sensors: Laboratory and free-living analysis of context-specific submaximal heart rates

2Development of land use regression models for PM2. 5, PM2. 5 absorbance, PM10 and PMcoarse in 20 European study areas; results of the ESCAPE project

2Multi-dimensional regression analysis of time-series data streams

1Gaussian processes for regression

просмотр

сходные по теме тэги

концепции

тэги

закладки (спрятать)15 показатьвсётолько закладкизакладки на страницу5102050100 RSSBibTeXXML

публикации (спрятать)28 показатьвсётолько публикациипубликации на страницу5102050100 расширенный... RSSBibTeXRDFдальше...

просмотр

сходные по теме тэги

тэги

закладки (спрятать)15
показать
всё
только закладки
закладки на страницу
5
10
20
50
100
RSS
BibTeX
XML

публикации (спрятать)28
показать
всё
только публикации
публикации на страницу
5
10
20
50
100
расширенный...
RSS
BibTeX
RDF
дальше...