Gotowa bibliografia na temat „Online ensemble regression”
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Artykuły w czasopismach na temat "Online ensemble regression"
Liu, Yang, Bo He, Diya Dong, Yue Shen, Tianhong Yan, Rui Nian i Amaury Lendasse. "Particle Swarm Optimization Based Selective Ensemble of Online Sequential Extreme Learning Machine". Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/504120.
Pełny tekst źródłaRahmawati, Eka, i Candra Agustina. "Implementasi Teknik Bagging untuk Peningkatan Kinerja J48 dan Logistic Regression dalam Prediksi Minat Pembelian Online". Jurnal Teknologi Informasi dan Terapan 7, nr 1 (9.06.2020): 16–19. http://dx.doi.org/10.25047/jtit.v7i1.123.
Pełny tekst źródłaHansrajh, Arvin, Timothy T. Adeliyi i Jeanette Wing. "Detection of Online Fake News Using Blending Ensemble Learning". Scientific Programming 2021 (28.07.2021): 1–10. http://dx.doi.org/10.1155/2021/3434458.
Pełny tekst źródłaZhang, Junbo, C. Y. Chung i Lin Guan. "Noise Effect and Noise-Assisted Ensemble Regression in Power System Online Sensitivity Identification". IEEE Transactions on Industrial Informatics 13, nr 5 (październik 2017): 2302–10. http://dx.doi.org/10.1109/tii.2017.2671351.
Pełny tekst źródłaAzeez, Nureni Ayofe, i Emad Fadhal. "Classification of Virtual Harassment on Social Networks Using Ensemble Learning Techniques". Applied Sciences 13, nr 7 (4.04.2023): 4570. http://dx.doi.org/10.3390/app13074570.
Pełny tekst źródłaBodyanskiy, Ye V., Kh V. Lipianina-Honcharenko i A. O. Sachenko. "ENSEMBLE OF ADAPTIVE PREDICTORS FOR MULTIVARIATE NONSTATIONARY SEQUENCES AND ITS ONLINE LEARNING". Radio Electronics, Computer Science, Control, nr 4 (2.01.2024): 91. http://dx.doi.org/10.15588/1607-3274-2023-4-9.
Pełny tekst źródłaR, Chitra A., i Dr Arjun B. C. "Performance Analysis of Regression Algorithms for Used Car Price Prediction: KNIME Analytics Platform". International Journal for Research in Applied Science and Engineering Technology 11, nr 2 (28.02.2023): 1324–31. http://dx.doi.org/10.22214/ijraset.2023.49180.
Pełny tekst źródłaSetiawan, Yahya, Jondri Jondri i Widi Astuti. "Twitter Sentiment Analysis on Online Transportation in Indonesia Using Ensemble Stacking". JURNAL MEDIA INFORMATIKA BUDIDARMA 6, nr 3 (25.07.2022): 1452. http://dx.doi.org/10.30865/mib.v6i3.4359.
Pełny tekst źródłade Almeida, Ricardo, Yee Mey Goh, Radmehr Monfared, Maria Teresinha Arns Steiner i Andrew West. "An ensemble based on neural networks with random weights for online data stream regression". Soft Computing 24, nr 13 (9.11.2019): 9835–55. http://dx.doi.org/10.1007/s00500-019-04499-x.
Pełny tekst źródłaKothapalli. Mandakini, Et al. "Ensemble Learning for fraud detection in Online Payment System". International Journal on Recent and Innovation Trends in Computing and Communication 11, nr 10 (2.11.2023): 1070–76. http://dx.doi.org/10.17762/ijritcc.v11i10.8626.
Pełny tekst źródłaRozprawy doktorskie na temat "Online ensemble regression"
Conesa, Gago Agustin. "Methods to combine predictions from ensemble learning in multivariate forecasting". Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-103600.
Pełny tekst źródłaPeng, Tao. "Analyse de données loT en flux". Electronic Thesis or Diss., Aix-Marseille, 2021. http://www.theses.fr/2021AIXM0649.
Pełny tekst źródłaSince the advent of the IoT (Internet of Things), we have witnessed an unprecedented growth in the amount of data generated by sensors. To exploit this data, we first need to model it, and then we need to develop analytical algorithms to process it. For the imputation of missing data from a sensor f, we propose ISTM (Incremental Space-Time Model), an incremental multiple linear regression model adapted to non-stationary data streams. ISTM updates its model by selecting: 1) data from sensors located in the neighborhood of f, and 2) the near-past most recent data gathered from f. To evaluate data trustworthiness, we propose DTOM (Data Trustworthiness Online Model), a prediction model that relies on online regression ensemble methods such as AddExp (Additive Expert) and BNNRW (Bagging NNRW) for assigning a trust score in real time. DTOM consists: 1) an initialization phase, 2) an estimation phase, and 3) a heuristic update phase. Finally, we are interested predicting multiple outputs STS in presence of imbalanced data, i.e. when there are more instances in one value interval than in another. We propose MORSTS, an online regression ensemble method, with specific features: 1) the sub-models are multiple output, 2) adoption of a cost sensitive strategy i.e. the incorrectly predicted instance has a higher weight, and 3) management of over-fitting by means of k-fold cross-validation. Experimentation with with real data has been conducted and the results were compared with reknown techniques
Części książek na temat "Online ensemble regression"
Osojnik, Aljaž, Panče Panov i Sašo Džeroski. "iSOUP-SymRF: Symbolic Feature Ranking with Random Forests in Online Multi-target Regression". W Discovery Science, 48–63. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45275-8_4.
Pełny tekst źródłaDuda, Piotr, Maciej Jaworski i Leszek Rutkowski. "Online GRNN-Based Ensembles for Regression on Evolving Data Streams". W Advances in Neural Networks – ISNN 2018, 221–28. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92537-0_26.
Pełny tekst źródłaNazeer, Ishrat, Mamoon Rashid, Sachin Kumar Gupta i Abhishek Kumar. "Use of Novel Ensemble Machine Learning Approach for Social Media Sentiment Analysis". W Advances in Social Networking and Online Communities, 16–28. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-4718-2.ch002.
Pełny tekst źródłaRajkumar S., Mary Nikitha K., Ramanathan L., Rajasekar Ramalingam i Mudit Jantwal. "Cloud Hosted Ensemble Learning-Based Rental Apartment Price Prediction Model Using Stacking Technique". W Deep Learning Research Applications for Natural Language Processing, 229–38. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-6001-6.ch015.
Pełny tekst źródłaStreszczenia konferencji na temat "Online ensemble regression"
Kuzin, Danil, Le Yang, Olga Isupova i Lyudmila Mihaylova. "Ensemble Kalman Filtering for Online Gaussian Process Regression and Learning". W 2018 International Conference on Information Fusion (FUSION). IEEE, 2018. http://dx.doi.org/10.23919/icif.2018.8455785.
Pełny tekst źródłaXu, Jianpeng, Pang-Ning Tan i Lifeng Luo. "ORION: Online Regularized Multi-task Regression and Its Application to Ensemble Forecasting". W 2014 IEEE International Conference on Data Mining (ICDM). IEEE, 2014. http://dx.doi.org/10.1109/icdm.2014.90.
Pełny tekst źródłaL. Grim, Luis Fernando, i Andre Leon S. Gradvohl. "High-Performance Ensembles of Online Sequential Extreme Learning Machine for Regression and Time Series Forecasting". W 2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD). IEEE, 2018. http://dx.doi.org/10.1109/cahpc.2018.8645863.
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