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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
, Oral Okan, Alper Özbilen, Seyda Ertekin, Suat Özdemir
:
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
, Seyda Ertekin:
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
, Aysenur Birturk
, Sinan Eren, Dilek Küçük
:
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 2024-07-20 20:28 CEST by the dblp team
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