RETRACTED: Bayesian approach to incremental batch learning on forest cover sensor data for multiclass classification

Andrade M Gasca E Rend´on E (2017) Implementation of incremental learning in artificial neural networks. In: Global conference on artificial intelligence, Miami, USA, 2017, October. pp. 221–232, Vol. 50.

Athilakshmi R Rajavel R Jacob SG (2018) A survey on deep-learning architectures. Journal of Computational and Theoretical Nanoscience 15(8): 2577–2579.

Bayesian Machine Learning (2016). http://fastml.com/bayesian-machine-learning (accessed 10 June 2020)

Castro FM Mar´ın-Jim´enez MJ Guil N , et al. (2018). End-to-end incremental learning. In: Proceedings of the European conference on computer vision, Glasgow, UK, 23–28 August 2020, pp. 233–248.

Carneiro T Medeiros Da Nobrega RV Nepomuceno T , et al. (2018) Performance analysis of googlecolaboratory as a tool for accelerating deep learning applications. IEEE Access 6: 61677–61685. DOI: 10.1109/access.2018.2874767

Creme (2020) Creme-incremental learrning in Python. https://creme-ml.github.io/ (accessed 09 July 2020)

Graves A (2011) Practical variational inference for neural networks. Advances in Neural Information Processing Systems. ACM, 2348–2356.

Gupta A Thakur HK Shrivastava R , et al. (2017) A big data analysis framework using apache spark and deep learning. In: 2017 IEEE international conference on data mining workshops (ICDMW), New Orleans, LA, USA, 18–21 November 2017, pp. 9–16. DOI: 10.1109/icdmw.2017.9

Kochurov M Garipov T Podoprikhin D , et al (2018) Bayesian incremental learning for deep neural networks. arXiv preprint (2018). arXiv:1802.07329.

Law A Ghosh A (2019) Multi-label classification using a cascade of stacked autoencoder and extreme learning machines. Neurocomputing 358: 222–234. DOI: 10.1016/j.neucom.2019.05.051

UCI (2020) https://archive.ics.uci.edu/ml/datasets/covertype (Last accessed Date: 04 June 2020)

MXNET (2020) MXNET library for deep learning. https://mxnet.incubator.apache.org/ (accessed 01 June 2020)

Pathmind (2020a) Neural Networks and Deep Learning. https://pathmind.com/wiki/neural-network (accessed 09 July 2020)

Pathmind (2020b) Perfomance Measures. https://pathmind.com/wiki/neural-network (accessed 09 July 2020)

Google Colab (2019). https://www.geeksforgeeks.org/how-to-use-google-colab/ (accessed 10 July 2020)

Park H Lee K (2019) Adaptive natural gradient method for learning neural networks with large data set in mini-batch mode. In: 2019 international conference on artificial intelligence in information and communication (ICAIIC),Okinawa, Japan, 2019, February, pp. 306–310. DOI: 10.1109/icaiic.2019.8669082

Patel S Patel A (2018) Deep leaning architectures and its applications: a survey. International Journal of Computer Sciences and Engineering 6(6): 1177–1183. DOI: 10.26438/ijcse/v6i6.11771183

Pf¨ulb B Gepperth A Abdullah S , et al. (2018) Catastrophic forgetting: still a problem for DNNs. In: International conference on artificial neural networks, Greece, 2018, October, pp. 487–497. Cham: Springer. DOI: 10.1007/978-3-030-01418-6_48

Sismanoglu G Onde MA Kocer F , et al. (2019) Deep learning basedforecasting in stock market with big data analytics. In: 2019 scientific meeting on electrical-electronics & biomedical engineering and computer science (EBBT), Turkey, 2019, April. pp. 1–4, DOI: 10.1109/bigdata47090.2019.9005523

Yu J Zheng X Wang S (2019) A deep autoencoder feature learning method for process pattern recognition. Journal of Process Control 79: 1–15. DOI: 10.1016/j.jprocont.2019.05.002

Zareapoor M Shamsolmoali P Kumar Jain D , et al. (2018) Kernelized support vector machine with deep learning: an efficient approach for extreme multiclass dataset. Pattern Recognition Letters 115: 4–13. DOI: 10.1016/j.patrec.2017.09.018

Towards Data Science (2018) Deep learning with apache spark — part 1. https://towardsdatascience.com/deep-learning-with-apache-spark-part-1- 6d397c16abd (Last accessed Date: 14 August 2019)