References - And understanding agents

References:
Wang, F., Wang, Q., Nie, F., Yu, W., Wang, R. (2018). Efficient tree classifiers for large scale datasets. Neurocomputing, 284, 70-79.
UC Irvine Machine Learning Repository: https://archive.ics.uci.edu/ml/index.php.
Machine Learning Repository: https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets
Pinto, A., Pereira, S., Rasteiro, D.M., Silva, C. (2018). Hierarchical brain tumour segmentation using extremely randomized trees. Pattern Recognit, 82, 105-117.
Vanli, N.D., Sayin, M.O., Mohaghegh, N.M., Ozkan, H., Kozat, S.S. (2019). Nonlinear regression via incremental decision trees, Pattern Recognit, 86, 1-13.
Kotsiantis, S.B. (2013). Decision trees: a recent overview. Artif. Intell. Rev., 39 (4), 261-283.
Breiman, L., Friedman, J.H., Stone, C.J., Olshen, R.A. (1984). CART: Classification and Regression Trees, Chapman & Hall/CRC.
Quinlan, J.R. (1993). C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers Inc.
Breiman, L., Friedman, J.H., Stone, C.J., Olshen, R.A. (1984). CART: Classification and Regression Trees, Chapman & Hall/CRC.
Yildiz, C., Alpaydin, E. (2001). Omnivariate decision trees. IEEE Trans. Neural Netw., 12 (6), 1539-1546.
Nie, F., Zhu, W., Xuelong, L. (2020). Decision Tree SVM: An Extension of Linear SVM for Non-linear Classification. Neurocomputing, 401, 153-159. doi: https://doi.org/10.1016/j.neucom.2019.10.051
Blanco, A., Domingo, J., Martínez, S., Sánchez, D. (2020). Machine learning explainability via microaggregation and shallow decision trees. Knowledge-Based Systems, 194, 15-26. https://doi.org/10.1016/j.knosys.2020.105532
Marakhimov, A.R., Khudaybergenov, K.K. (2020). Approach to the synthesis of neural network structure during classification. International Journal of Computing, 19(1), 20-26. https://doi.org/10.47839/ijc.19.1.1689
Krizhevsky, A., Sutskever, I., Hinton, G.E. (2012). Imagenet classification with deep convolutional neural networks. In Proceedings of Advances in Neural Information Processing Systems, 1097-1105.
Goodfellow, I., Bengio, Y., Courville, A. (2016). Deep learning. MIT Press.
Frosst, N., Hinton, G. (2017). Distilling a neural network into a soft decision tree. arXiv preprint arXiv:1711.09784.
Wan, A., Dunlap, L., Ho, D., Yin, J., Lee, S., Jin, H., Petryk, S., Bargal, S.A., Gonzalez, J.E. (2020). Nbdt: Neural-backed decision trees. arXiv preprint arXiv:2004.00221.
Pablo, M., Jenny, A.C., Rosa, E.L., Iñaki, U. (2021). Towards a mathematical framework to inform neural network modelling via polynomial regression. Neural Networks, 142, 57-72. https://doi.org/10.1016/j.neunet.2021.04.036
Wenzhi, C., Vahid, M., Raschka, S. (2020). Rank consistent ordinal regression for neural networks with application to age estimation. Pattern Recognition Letters, 140, 325-331. https://doi.org/10.1016/j.patrec.2020.11.008
Messner, E., Fediuk, M., Swatek, P., Scheidl, S., Smolle-Jüttner, F.M., Olschewski, H., Pernkopf, F. (2020). Multi-channel lung sound classification with convolutional recurrent neural networks. Computers in Biology and Medicine, 122, 1-11. https://doi.org/10.1016/j.compbiomed.2020.103831
Youling, L. (2020). A calibration method of computer vision system based on dual attention mechanism. Image and Vision Computing, 103, 104039. https://doi.org/10.1016/j.imavis.2020.104039
Jeremiah, B., Zhou, Y., Chan, H.M. (2020). Comparing biological and artificial vision systems: Network measures of functional connectivity. Neuroscience Letters, 739, 135407. https://doi.org/10.1016/j.neulet.2020.135407
Shabbeer, S.H., Dubey, Sh.R., Pulabaigari, V., Mukherjee, S. (2020). Impact of fully connected layers on performance of convolutional neural networks for image classification. Neurocomputing, 378, 112-119, https://doi.org/10.1016/j.neucom.2019.10.008
Xu, M. (2020). Image processing system based on FPGA and convolutional neural network. Microprocessors and Microsystems, 2020, 103379. https://doi.org/10.1016/j.micpro.2020.103379 ..............................
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