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Seyda Ertekin
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2020 – today
- 2024
- [j11]Güven Orkun Tanik
, Seyda Ertekin:
Hierarchical reinforcement Thompson composition. Neural Comput. Appl. 36(20): 12317-12326 (2024) - 2022
- [j10]Egemen Sert
Linking COVID-19 Perception With Socioeconomic Conditions Using Twitter Data. IEEE Trans. Comput. Soc. Syst. 9(2): 394-405 (2022) - [c22]Pinar Ezgi Duymus, Seyda Ertekin:
Alleviating Mode Collapse Problem in Deep Bayesian Active Learning. SIU 2022: 1-4 - [c21]Güven Orkun Tanik, Seyda Ertekin:
Recursive Compositional Reinforcement Learning for Continuous Control. SIU 2022: 1-4 - 2021
- [c20]Pinar Ezgi Çöl, Seyda Ertekin:
Feature Dimensionality Reduction with Variational Autoencoders in Deep Bayesian Active Learning. SIU 2021: 1-4
2010 – 2019
- 2019
- [j9]Ümit Çavus Büyüksahin
Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition. Neurocomputing 361: 151-163 (2019) - [c19]Mehmet Dinç, Seyda Ertekin, Hadi Özkan, Can Meydanli, Volkan Atalay
Forecasting of Product Quality Through Anomaly Detection. PKDD/ECML Workshops (2) 2019: 357-366 - [c18]Ümit Çavus Büyüksahin, Seyda Ertekin:
Prediction Model Selection with Frequency Check on Decomposed Time Series. SIU 2019: 1-4 - 2018
- [j8]Ramin Moghaddass, Seyda Ertekin:
Joint optimization of ordering and maintenance with condition monitoring data. Ann. Oper. Res. 263(1-2): 271-310 (2018) - [j7]Cynthia Rudin, Seyda Ertekin:
Learning customized and optimized lists of rules with mathematical programming. Math. Program. Comput. 10(4): 659-702 (2018) - [c17]Yusuf Arslan, Aybike Simsek Dilbaz, Seyda Ertekin, Pinar Karagoz
Short-Term Electricity Consumption Forecast Using Datasets of Various Granularities. DARE@PKDD/ECML 2018: 116-126 - [c16]Ümit Cavus Büyüksahin, Seyda Ertekin:
Time series forecasting using empirical mode decomposition and hybrid method. SIU 2018: 1-4 - [c15]Memduh Cagri Demir, Seyda Ertekin:
Identifying textual personal information using bidirectional LSTM networks. SIU 2018: 1-4 - [i2]Ümit Çavus Büyüksahin, Seyda Ertekin:
Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition. CoRR abs/1812.11526 (2018) - 2017
- [c14]Egemen Sert, Seyda Ertekin, Ugur Halici
Ensemble of convolutional neural networks for classification of breast microcalcification from mammograms. EMBC 2017: 689-692 - [c13]Salva Daneshgadeh, Nazife Baykal, Seyda Ertekin:
DDoS Attack Modeling and Detection Using SMO. ICMLA 2017: 432-436 - 2015
- [j6]Seyda Ertekin, Cynthia Rudin:
A Bayesian Approach to Learning Scoring Systems. Big Data 3(4): 267-276 (2015) - 2014
- [j5]Seyda Ertekin, Cynthia Rudin, Haym Hirsh:
Approximating the crowd. Data Min. Knowl. Discov. 28(5-6): 1189-1221 (2014) - [j4]Cynthia Rudin, Seyda Ertekin, Rebecca J. Passonneau, Axinia Radeva, Ashish Tomar, Boyi Xie, Stanley Lewis, Mark Riddle, Debbie Pangsrivinij, Tyler H. McCormick
Analytics for Power Grid Distribution Reliability in New York City. Interfaces 44(4): 364-383 (2014) - 2013
- [c12]Seyda Ertekin, Cynthia Rudin, Tyler H. McCormick:
Predicting Power Failures with Reactive Point Processes. AAAI (Late-Breaking Developments) 2013 - [c11]Seyda Ertekin:
Adaptive Oversampling for Imbalanced Data Classification. ISCIS 2013: 261-269 - 2012
- [c10]Seyda Ertekin, Haym Hirsh, Cynthia Rudin:
Selective Sampling of Labelers for Approximating the Crowd. AAAI Fall Symposium: Machine Aggregation of Human Judgment 2012 - [i1]Seyda Ertekin, Haym Hirsh, Cynthia Rudin:
Learning to Predict the Wisdom of Crowds. CoRR abs/1204.3611 (2012) - 2011
- [j3]Seyda Ertekin, Cynthia Rudin:
On Equivalence Relationships Between Classification and Ranking Algorithms. J. Mach. Learn. Res. 12: 2905-2929 (2011) - [j2]Seyda Ertekin, Léon Bottou, C. Lee Giles
Nonconvex Online Support Vector Machines. IEEE Trans. Pattern Anal. Mach. Intell. 33(2): 368-381 (2011)
2000 – 2009
- 2009
- [c9]Levent Bolelli, Seyda Ertekin, C. Lee Giles:
Topic and Trend Detection in Text Collections Using Latent Dirichlet Allocation. ECIR 2009: 776-780 - [c8]Levent Bolelli, Seyda Ertekin, Ding Zhou, C. Lee Giles
Finding topic trends in digital libraries. JCDL 2009: 69-72 - 2007
- [c7]Seyda Ertekin, Jian Huang, Léon Bottou, C. Lee Giles
Learning on the border: active learning in imbalanced data classification. CIKM 2007: 127-136 - [c6]Jian Huang, Seyda Ertekin, Yang Song, Hongyuan Zha, C. Lee Giles
Efficient Multiclass Boosting Classification with Active Learning. SDM 2007: 297-308 - [c5]Seyda Ertekin, Jian Huang, C. Lee Giles
Active learning for class imbalance problem. SIGIR 2007: 823-824 - [c4]Levent Bolelli, Seyda Ertekin, Ding Zhou, C. Lee Giles
K-SVMeans: A Hybrid Clustering Algorithm for Multi-Type Interrelated Datasets. Web Intelligence 2007: 198-204 - [c3]Levent Bolelli, Seyda Ertekin, Ding Zhou, C. Lee Giles
A clustering method for web data with multi-type interrelated components. WWW 2007: 1121-1122 - 2006
- [c2]Levent Bolelli, Seyda Ertekin, C. Lee Giles:
Clustering Scientific Literature Using Sparse Citation Graph Analysis. PKDD 2006: 30-41 - [c1]Jian Huang, Seyda Ertekin, C. Lee Giles:
Efficient Name Disambiguation for Large-Scale Databases. PKDD 2006: 536-544 - 2005
- [j1]Antoine Bordes, Seyda Ertekin, Jason Weston, Léon Bottou:
Fast Kernel Classifiers with Online and Active Learning. J. Mach. Learn. Res. 6: 1579-1619 (2005)
Coauthor Index
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last updated on 2024-09-13 00:39 CEST by the dblp team
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