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1WISHFUL - Website Extraction of Institutional Sources with Heterogeneous Factors and User-Driven Linkage
S. Shahania, M. Spiliopoulou, and D. Broneske. Information Integration and Web Intelligence: 25th International Conference, IiWAS 2023, Denpasar, Bali, Indonesia, December 4–6, 2023, Proceedings, page 20–26. Berlin, Heidelberg, Springer-Verlag, (2023)
2 months ago by @kmd-ovgu
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myown
Web_Scraping
Job_Ads
from:kmd-ovgu
Heterogeneous_Information
myownWeb_ScrapingJob_Adsfrom:kmd-ovguHeterogeneous_Information
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2Joining Imputation and Active Feature Acquisition for Cost Saving on Data Streams with Missing Features
M. Büttner, C. Beyer, and M. Spiliopoulou. Discovery Science, page 308-322. Springer Nature Switzerland, (2023)
11 months ago by @kmd-ovgu
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myown
stream-mining
data-streams
imputation
active-feature-acquisition
myownstream-miningdata-streamsimputationactive-feature-acquisition
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2Active feature acquisition on data streams under feature drift.
C. Beyer, M. Büttner, V. Unnikrishnan, M. Schleicher, E. Ntoutsi, and M. Spiliopoulou. Ann. des Télécommunications, 75 (9-10): 597-611 (2020)
2 years ago by @kmd-ovgu
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kmd
myown
stream-mining
active-feature-acquisition
kmdmyownstream-miningactive-feature-acquisition
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3Resource management for model learning at entity level
C. Beyer, V. Unnikrishnan, R. Brüggemann, V. Toulouse, H. Omar, E. Ntoutsi, and M. Spiliopoulou. Annals of Telecommunications, 75 (9-10): 549--561 (August 2020)
2 years ago by @kmd-ovgu
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kmd
myown
stream-mining
entity-centric-learning
kmdmyownstream-miningentity-centric-learning
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2Discovery of Patient Phenotypes through Multi-layer Network Analysis on the Example of Tinnitus
C. Puga, U. Niemann, V. Unnikrishnan, M. Schleicher, W. Schlee, and M. Spiliopoulou. 2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA), (2021)
3 years ago by @kmd-ovgu
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uniti
kmd
myown
tinnitus
medical_mining
unitikmdmyowntinnitusmedical_mining
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4User-centric vs whole-stream learning for EMA prediction
S. Shahania, V. Unnikrishnan, R. Pryss, R. Kraft, J. Schobel, R. Hannemann, W. Schlee, and M. Spiliopoulou. 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), page 307-312. (June 2021)
3 years ago by @kmd-ovgu
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tinnitus
medical_mining
kmdmyowntinnitusmedical_mining
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4Love thy Neighbours: A Framework for Error-Driven Discovery of Useful Neighbourhoods for One-Step Forecasts on EMA data
V. Unnikrishnan, Y. Shah, M. Schleicher, C. Fernández-Viadero, M. Strandzheva, D. Velikova, P. Dimitrov, R. Pryss, J. Schobel, W. Schlee and 1 other author(s). 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), page 295-300. (June 2021)
3 years ago by @kmd-ovgu
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kmd
myown
tinnitus
medical_mining
kmdmyowntinnitusmedical_mining
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4Circadian Conditional Granger Causalities on Ecological Momentary Assessment Data from an mHealth App
N. Jamaludeen, V. Unnikrishnan, R. Pryss, J. Schobel, W. Schlee, and M. Spiliopoulou. 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), page 354-359. (2021)
3 years ago by @kmd-ovgu
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kmd
myown
tinnitus
medical_mining
kmdmyowntinnitusmedical_mining
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2Juxtaposing 5G Coronavirus Tweets with General Coronavirus Tweets During the Early Months of Coronavirus Outbreak
R. Trad, and M. Spiliopoulou. 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), page 597-602. (2021)
3 years ago by @kmd-ovgu
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5g
myown
twitter
coronavirus
kmd5gmyowntwittercoronavirus
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2A Framework for Authorial Clustering of Shorter Texts in Latent Semantic Spaces
R. Trad, and M. Spiliopoulou. Advances in Intelligent Data Analysis XIX, page 301--312. Cham, Springer International Publishing, (2021)
4 years ago by @kmd-ovgu
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myown
kmdmyown
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3Understanding adherence to the recording of ecological momentary assessments in the example of tinnitus monitoring
M. Schleicher, V. Unnikrishnan, P. Neff, J. Simoes, T. Probst, R. Pryss, W. Schlee, and M. Spiliopoulou. Scientific Reports, (December 2020)
4 years ago by @kmd-ovgu
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tinnitus
adherence
mHealth
medical_mining
myowntinnitusadherencemHealthmedical_mining
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4Predicting the Health Condition of mHealth App Users with Large Differences in the Number of Recorded Observations - Where to Learn from?
V. Unnikrishnan, Y. Shah, M. Schleicher, M. Strandzheva, P. Dimitrov, D. Velikova, R. Pryss, J. Schobel, W. Schlee, and M. Spiliopoulou. DS, volume 12323 of Lecture Notes in Computer Science, page 659-673. Springer, (2020)
4 years ago by @kmd-ovgu
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myown
mHealth
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kmdmyownmHealthmedical_mining
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2Multivariate Time Series as Images: Imputation Using Convolutional Denoising Autoencoder
A. Safi, C. Beyer, V. Unnikrishnan, and M. Spiliopoulou. Advances in Intelligent Data Analysis XVIII, page 1--13. Cham, Springer International Publishing, (2020)
4 years ago by @kmd-ovgu
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myown
deep-learning
time-series
imputation
myowndeep-learningtime-seriesimputation
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1Resource management for model learning at entity level
C. Beyer, V. Unnikrishnan, R. Brüggemann, V. Toulouse, H. Omar, E. Ntoutsi, and M. Spiliopoulou. Annals of Telecommunications, (Aug 29, 2020)
4 years ago by @kmd-ovgu
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myown
stream-mining
entity-centric-learning
myownstream-miningentity-centric-learning
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1Active feature acquisition on data streams under feature drift
C. Beyer, M. Büttner, V. Unnikrishnan, M. Schleicher, E. Ntoutsi, and M. Spiliopoulou. Annals of Telecommunications, (Jul 8, 2020)
4 years ago by @kmd-ovgu
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myown
stream-mining
active-learning
active-feature-acquisition
myownstream-miningactive-learningactive-feature-acquisition
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2Temporal Density Extrapolation using a Dynamic Basis Approach.
G. Krempl, D. Lang, and V. Hofer. Data Mining and Knowledge Discovery, (2019)
6 years ago by @gk
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myown
myown
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1A Framework for Expert-Driven Subpopulation Discovery and Evaluation Using Subspace Clustering for Epidemiological Data
T. Hielscher, U. Niemann, B. Preim, H. Völzke, T. Ittermann, and M. Spiliopoulou. Expert Systems with Applications, (2018)
6 years ago by @thielscher
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kmd
myown
medical_mining
kmdmyownmedical_mining
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1Personalized recommender systems for product-line configuration processes
J. Pereira, P. Matuszyk, S. Krieter, M. Spiliopoulou, and G. Saake. Computer Languages, Systems & Structures, (2018)
7 years ago by @matuszyk
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myown
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1Scalable Online Top-N Recommender Systems
A. Jorge, J. Vinagre, M. Domingues, J. Gama, C. Soares, P. Matuszyk, and M. Spiliopoulou. page 3--20. Springer International Publishing, (2017)
7 years ago by @matuszyk
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myown
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1Forgetting techniques for stream-based matrix factorization in recommender systems
P. Matuszyk, J. Vinagre, M. Spiliopoulou, A. Jorge, and J. Gama. Knowledge and Information Systems, (Aug 4, 2017)
7 years ago by @matuszyk
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myown
myown
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BibSonomy

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publications (hide)64
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1WISHFUL - Website Extraction of Institutional Sources with Heterogeneous Factors and User-Driven Linkage

2Joining Imputation and Active Feature Acquisition for Cost Saving on Data Streams with Missing Features

2Active feature acquisition on data streams under feature drift.

3Resource management for model learning at entity level

2Discovery of Patient Phenotypes through Multi-layer Network Analysis on the Example of Tinnitus

4User-centric vs whole-stream learning for EMA prediction

4Love thy Neighbours: A Framework for Error-Driven Discovery of Useful Neighbourhoods for One-Step Forecasts on EMA data

4Circadian Conditional Granger Causalities on Ecological Momentary Assessment Data from an mHealth App

2Juxtaposing 5G Coronavirus Tweets with General Coronavirus Tweets During the Early Months of Coronavirus Outbreak

2A Framework for Authorial Clustering of Shorter Texts in Latent Semantic Spaces

3Understanding adherence to the recording of ecological momentary assessments in the example of tinnitus monitoring

4Predicting the Health Condition of mHealth App Users with Large Differences in the Number of Recorded Observations - Where to Learn from?

2Multivariate Time Series as Images: Imputation Using Convolutional Denoising Autoencoder

1Resource management for model learning at entity level

1Active feature acquisition on data streams under feature drift

2Temporal Density Extrapolation using a Dynamic Basis Approach.

1A Framework for Expert-Driven Subpopulation Discovery and Evaluation Using Subspace Clustering for Epidemiological Data

1Personalized recommender systems for product-line configuration processes

1Scalable Online Top-N Recommender Systems

1Forgetting techniques for stream-based matrix factorization in recommender systems

Knowledge Management and Discovery Lab (KMD), OVGU Magdeburg

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Knowledge Management and Discovery Lab (KMD), OVGU Magdeburg

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