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ORCIDJafar Tanha
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2020 – today
- 2022
[j11]Jafar Tanha
, Negin Samadi, Yousef Abdi, Nazila Razzaghi-Asl:
CPSSDS: Conformal prediction for semi-supervised classification on data streams. Inf. Sci. 584: 212-234 (2022)- [j10]Shadi Alijani, Jafar Tanha, Leyli Mohammadkhanli:
An ensemble of deep learning algorithms for popularity prediction of flickr images. Multim. Tools Appl. 81(3): 3253-3274 (2022) - [c11]Negin Samadi, Jafar Tanha, Sahar Hassanzadeh Mostafaei, Nazila Razzaghi-Asl, Soodabeh Imanzadeh:
Spreader node detection based on the Perron-Frobenius theorem in complex networks. CSICC 2022: 1-5 - 2021
- [j9]Mohammad Saber Iraji, Mohammad-Reza Feizi-Derakhshi, Jafar Tanha:
COVID-19 Detection Using Deep Convolutional Neural Networks and Binary Differential Algorithm-Based Feature Selection from X-Ray Images. Complex. 2021: 9973277:1-9973277:10 (2021) - [j8]Shirin Khezri, Jafar Tanha, Ali Ahmadi, Arash Sharifi:
A novel semi-supervised ensemble algorithm using a performance-based selection metric to non-stationary data streams. Neurocomputing 442: 125-145 (2021) - [j7]Mona Emadi, Jafar Tanha, Mohammad Ebrahim Shiri, Mehdi Hosseinzadeh Aghdam:
A Selection Metric for semi-supervised learning based on neighborhood construction. Inf. Process. Manag. 58(2): 102444 (2021) - [i1]Mohammad Saber Iraji, Mohammad-Reza Feizi-Derakhshi, Jafar Tanha:
Deep learning for COVID-19 diagnosis based feature selection using binary differential evolution algorithm. CoRR abs/2104.07279 (2021) - 2020
- [j6]Shirin Khezri, Jafar Tanha, Ali Ahmadi, Arash Sharifi:
STDS: self-training data streams for mining limited labeled data in non-stationary environment. Appl. Intell. 50(5): 1448-1467 (2020) - [j5]Jafar Tanha, Yousef Abdi, Negin Samadi, Nazila Razzaghi, Mohammad Asadpour:
Boosting methods for multi-class imbalanced data classification: an experimental review. J. Big Data 7(1): 70 (2020) - [c10]Mona Emadi, Jafar Tanha:
Margin-Based Semi-supervised Learning Using Apollonius Circle. TTCS 2020: 48-60
2010 – 2019
- 2019
- [j4]Jafar Tanha:
A multiclass boosting algorithm to labeled and unlabeled data. Int. J. Mach. Learn. Cybern. 10(12): 3647-3665 (2019) - 2018
- [j3]Jafar Tanha:
MSSBoost: A new multiclass boosting to semi-supervised learning. Neurocomputing 314: 251-266 (2018) - 2017
- [j2]Jafar Tanha, Maarten van Someren, Hamideh Afsarmanesh:
Semi-supervised self-training for decision tree classifiers. Int. J. Mach. Learn. Cybern. 8(1): 355-370 (2017) - 2015
- [c9]Jafar Tanha, Jesse de Does, Katrien Depuydt:
Combining higher-order N-grams and intelligent sample selection to improve language modeling for Handwritten Text Recognition. ESANN 2015 - [c8]Jafar Tanha, Jesse de Does, Katrien Depuydt, Joan-Andreu Sánchez:
Crossing the lines: making optimal use of context in line-based Handwritten Text Recognition. ICDAR 2015: 956-960 - [c7]Jafar Tanha, Jesse de Does, Katrien Depuydt:
An LDA-based Topic Selection Approach to Language Model Adaptation for Handwritten Text Recognition. RANLP 2015: 646-653 - 2014
- [j1]Jafar Tanha, Maarten van Someren, Hamideh Afsarmanesh:
Boosting for multiclass semi-supervised learning. Pattern Recognit. Lett. 37: 63-77 (2014) - [c6]Jafar Tanha, Jesse de Does, Katrien Depuydt:
An Intelligent Sample Selection Approach to Language Model Adaptation for Hand-Written Text Recognition. ICFHR 2014: 349-354 - 2013
- [c5]Jafar Tanha, Mohammad Javad Saberian, Maarten van Someren:
Multiclass Semi-Supervised Boosting Using Similarity Learning. ICDM 2013: 1205-1210 - 2012
- [c4]Jafar Tanha, Maarten van Someren, Hamideh Afsarmanesh:
An AdaBoost Algorithm for Multiclass Semi-supervised Learning. ICDM 2012: 1116-1121 - [c3]Jafar Tanha, Maarten van Someren, Merijn de Bakker, Willem Bouteny, Judy Shamoun-Baranesy, Hamideh Afsarmanesh:
Multiclass Semi-supervised Learning for Animal Behavior Recognition from Accelerometer Data. ICTAI 2012: 690-697 - 2011
- [c2]Jafar Tanha, Maarten van Someren, Hamideh Afsarmanesh:
Disagreement-Based Co-training. ICTAI 2011: 803-810 - 2010
- [c1]Hamideh Afsarmanesh, Jafar Tanha:
A High Level Architecture for Personalized Learning in Collaborative Networks. PRO-VE 2010: 601-608
Coauthor Index
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