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url = {http://www.sciencedirect.com/science/article/pii/S0168900206005341},
author = {X. Fan and D.J. Parker and M.D. Smith},
keywords = {\{PEPT\}},
keywords = {Tracer labelling},
keywords = {Ion-exchange},
keywords = {Tracer property},
keywords = {Direct activation}
}

@article{Volkwyn-UCT,
title = "Studies of the effect of tracer activity on time-averaged positron emission particle tracking measurements on tumbling mills at \{PEPT\} Cape Town ",
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volume = "24",
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pages = "261 - 266",
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keywords = "Comminution",
keywords = "Positron emission particle tracking",
keywords = "Tumbling mills "
}

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keywords = "\{PEPT\}",
keywords = "Surface chemistry",
keywords = "Tracer labelling",
keywords = "F-18 "
}

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title = "Positron emission particle tracking measurements with 50 micron tracers ",
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keywords = "\{PEPT\}",
keywords = "Ion exchange",
keywords = "Mixing",
keywords = "Particle tracking",
keywords = "Imaging",
keywords = "Radiation "
}

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author = "David J. Parker and Xianfeng Fan",
keywords = "\{PEPT\}",
keywords = "Tracer",
keywords = "Algorithm",
keywords = "Tracking "
}

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keywords = "Multi-phase flow",
keywords = "Turbulence",
keywords = "Visualization",
keywords = "Hydrocyclone",
keywords = "Positron emission particle tracking "
}

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@article{SAIP2016c,
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@inproceedings{CookMNH2016UGtc,
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}

@phdthesis{cook_thesis,
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        school = {University of Johannesburg},
        type = {PhD thesis}
}

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address = {New York, NY, USA},
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number = {3},
issn = {2157-6904},
url = {https://doi.org/10.1145/1961189.1961199},
doi = {10.1145/1961189.1961199},
abstract = {LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.},
journal = {ACM Trans. Intell. Syst. Technol.},
month = may,
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numpages = {27},
keywords = {Classification LIBSVM optimization regression support vector machines SVM}
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  author={Maulik, Ujjwal and Chakraborty, Debasis},
  journal={IEEE Geoscience and Remote Sensing Magazine}, 
  title={Remote Sensing Image Classification: A survey of support-vector-machine-based advanced techniques}, 
  year={2017},
  volume={5},
  number={1},
  pages={33-52},
  keywords={Training;Classification algorithms;Remote sensing;Support vector machines;Image classification;Data models},
  doi={10.1109/MGRS.2016.2641240}}

@misc{kaggle_2017,
        title = {2nd place solution for the 2017 national datascience bowl},
        year = {2017},
        author = {Julian Dewit},
        howpublished = {\url{https://juliandewit.github.io/kaggle-ndsb2017/}},
        note = {Accessed: 2024-07-29}
}


@InProceedings{ReLU_is_good,
  title = 	 {Deep Sparse Rectifier Neural Networks},
  author = 	 {Glorot, Xavier and Bordes, Antoine and Bengio, Yoshua},
  booktitle = 	 {Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics},
  pages = 	 {315--323},
  year = 	 {2011},
  editor = 	 {Gordon, Geoffrey and Dunson, David and Dudík, Miroslav},
  volume = 	 {15},
  series = 	 {Proceedings of Machine Learning Research},
  address = 	 {Fort Lauderdale, FL, USA},
  month = 	 {11--13 Apr},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v15/glorot11a/glorot11a.pdf},
  url = 	 {https://proceedings.mlr.press/v15/glorot11a.html},
  abstract = 	 {While logistic sigmoid neurons are more biologically plausible than hyperbolic tangent neurons, the latter work better for training multi-layer neural networks. This paper shows that rectifying neurons are an even better model of biological neurons and yield equal or better performance than hyperbolic tangent networks in spite of the hard non-linearity and non-differentiability at zero, creating sparse representations with true zeros which seem remarkably suitable for naturally sparse data. Even though they can take advantage of semi-supervised setups with extra-unlabeled data, deep rectifier networks can reach their best performance without requiring any unsupervised pre-training on purely supervised tasks with large labeled datasets. Hence, these results can be seen as a new milestone in the attempts at understanding the difficulty in training deep but purely supervised neural networks, and closing the performance gap between neural networks learnt with and without unsupervised pre-training.}
}
