Classical Machine Learning
@article{MLReview,
  title={Supervised machine learning: A review of classification techniques},
  author={Kotsiantis, Sotiris B and Zaharakis, I and Pintelas, P},
  journal={Emerging artificial intelligence applications in computer engineering},
  volume={160},
  pages={3--24},
  year={2007}
}
@techreport{knn,
  title={Discriminatory analysis-nonparametric discrimination: consistency properties},
  author={Fix, Evelyn and Hodges Jr, Joseph L},
  year={1951},
  institution={California Univ Berkeley}
}
@article{svm,
  title={Support-vector networks},
  author={Cortes, Corinna and Vapnik, Vladimir},
  journal={Machine learning},
  volume={20},
  number={3},
  pages={273--297},
  year={1995},
  publisher={Springer}
}
@inproceedings{svmnonlinear,
  title={A training algorithm for optimal margin classifiers},
  author={Boser, Bernhard E and Guyon, Isabelle M and Vapnik, Vladimir N},
  booktitle={Proceedings of the fifth annual workshop on Computational learning theory},
  pages={144--152},
  year={1992},
  organization={ACM}
}
@article{naivebayes,
  title={Idiot's Bayes—not so stupid after all?},
  author={Hand, David J and Yu, Keming},
  journal={International statistical review},
  volume={69},
  number={3},
  pages={385--398},
  year={2001},
  publisher={Wiley Online Library}
}
@article{randomforest,
  title={Classification and regression by randomForest},
  author={Liaw, Andy and Wiener, Matthew and others},
  journal={R news},
  volume={2},
  number={3},
  pages={18--22},
  year={2002}
}

Neural Networks
@article{lenet,
  title={Gradient-based learning applied to document recognition},
  author={LeCun, Yann and Bottou, L{\'e}on and Bengio, Yoshua and Haffner, Patrick},
  journal={Proceedings of the IEEE},
  volume={86},
  number={11},
  pages={2278--2324},
  year={1998},
  publisher={IEEE}
}
@inproceedings{alexnet,
  title={Imagenet classification with deep convolutional neural networks},
  author={Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E},
  booktitle={Advances in neural information processing systems},
  pages={1097--1105},
  year={2012}
}
@inproceedings{lenetVSalexnet,
  title={On the Performance of GoogLeNet and AlexNet Applied to Sketches.},
  author={Ballester, Pedro and de Ara{\'u}jo, Ricardo Matsumura},
  booktitle={AAAI},
  pages={1124--1128},
  year={2016}
}
@article{deepNN,
  title = "A survey of deep neural network architectures and their applications",
  journal = "Neurocomputing",
  volume = "234",
  pages = "11 - 26",
  year = "2017",
  issn = "0925-2312",
  doi = "https://doi.org/10.1016/j.neucom.2016.12.038",
  url = "http://www.sciencedirect.com/science/article/pii/S0925231216315533",
  author = "Weibo Liu and Zidong Wang and Xiaohui Liu and Nianyin Zeng and Yurong Liu and Fuad E. Alsaadi",
  keywords = "Autoencoder, Convolutional neural network, Deep learning, Deep belief network, Restricted Boltzmann machine"
}

MISC
@misc{openData,
  title={Open Database License (ODbL) v1.0},
  url={https://opendatacommons.org/licenses/odbl/1.0/},
  journal={Open Data Commons},
  year={2018},
  month={Feb}
}
@incollection{NIPS2012_4824,
title = {ImageNet Classification with Deep Convolutional Neural Networks},
author = {Alex Krizhevsky and Sutskever, Ilya and Hinton, Geoffrey E},
booktitle = {Advances in Neural Information Processing Systems 25},
editor = {F. Pereira and C. J. C. Burges and L. Bottou and K. Q. Weinberger},
pages = {1097--1105},
year = {2012},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf}
}
@ARTICLE{726791, 
author={Y. Lecun and L. Bottou and Y. Bengio and P. Haffner}, 
journal={Proceedings of the IEEE}, 
title={Gradient-based learning applied to document recognition}, 
year={1998}, 
volume={86}, 
number={11}, 
pages={2278-2324}, 
keywords={backpropagation;convolution;multilayer perceptrons;optical character recognition;2D shape variability;GTN;back-propagation;cheque reading;complex decision surface synthesis;convolutional neural network character recognizers;document recognition;document recognition systems;field extraction;gradient based learning technique;gradient-based learning;graph transformer networks;handwritten character recognition;handwritten digit recognition task;high-dimensional patterns;language modeling;multilayer neural networks;multimodule systems;performance measure minimization;segmentation recognition;Character recognition;Feature extraction;Hidden Markov models;Machine learning;Multi-layer neural network;Neural networks;Optical character recognition software;Optical computing;Pattern recognition;Principal component analysis}, 
doi={10.1109/5.726791}, 
ISSN={0018-9219}, 
month={Nov},}