145 lines
5.4 KiB
BibTeX
145 lines
5.4 KiB
BibTeX
Classical Machine Learning
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@article{MLReview,
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title={Supervised machine learning: A review of classification techniques},
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author={Kotsiantis, Sotiris B and Zaharakis, I and Pintelas, P},
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journal={Emerging artificial intelligence applications in computer engineering},
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volume={160},
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pages={3--24},
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year={2007}
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}
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@techreport{knn,
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title={Discriminatory analysis-nonparametric discrimination: consistency properties},
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author={Fix, Evelyn and Hodges Jr, Joseph L},
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year={1951},
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institution={California Univ Berkeley}
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}
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@article{svm,
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title={Support-vector networks},
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author={Cortes, Corinna and Vapnik, Vladimir},
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journal={Machine learning},
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volume={20},
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number={3},
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pages={273--297},
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year={1995},
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publisher={Springer}
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}
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@inproceedings{svmnonlinear,
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title={A training algorithm for optimal margin classifiers},
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author={Boser, Bernhard E and Guyon, Isabelle M and Vapnik, Vladimir N},
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booktitle={Proceedings of the fifth annual workshop on Computational learning theory},
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pages={144--152},
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year={1992},
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organization={ACM}
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}
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@article{naivebayes,
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title={Idiot's Bayes—not so stupid after all?},
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author={Hand, David J and Yu, Keming},
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journal={International statistical review},
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volume={69},
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number={3},
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pages={385--398},
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year={2001},
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publisher={Wiley Online Library}
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}
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@article{randomforest,
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title={Classification and regression by randomForest},
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author={Liaw, Andy and Wiener, Matthew and others},
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journal={R news},
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volume={2},
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number={3},
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pages={18--22},
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year={2002}
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}
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Neural Networks
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@article{lenet,
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title={Gradient-based learning applied to document recognition},
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author={LeCun, Yann and Bottou, L{\'e}on and Bengio, Yoshua and Haffner, Patrick},
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journal={Proceedings of the IEEE},
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volume={86},
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number={11},
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pages={2278--2324},
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year={1998},
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publisher={IEEE}
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}
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@inproceedings{alexnet,
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title={Imagenet classification with deep convolutional neural networks},
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author={Krizhevsky, Alex and Sutskever, Ilya and Hinton, Geoffrey E},
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booktitle={Advances in neural information processing systems},
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pages={1097--1105},
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year={2012}
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}
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@inproceedings{lenetVSalexnet,
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title={On the Performance of GoogLeNet and AlexNet Applied to Sketches.},
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author={Ballester, Pedro and de Ara{\'u}jo, Ricardo Matsumura},
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booktitle={AAAI},
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pages={1124--1128},
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year={2016}
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}
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@article{deepNN,
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title = "A survey of deep neural network architectures and their applications",
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journal = "Neurocomputing",
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volume = "234",
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pages = "11 - 26",
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year = "2017",
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issn = "0925-2312",
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doi = "https://doi.org/10.1016/j.neucom.2016.12.038",
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url = "http://www.sciencedirect.com/science/article/pii/S0925231216315533",
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author = "Weibo Liu and Zidong Wang and Xiaohui Liu and Nianyin Zeng and Yurong Liu and Fuad E. Alsaadi",
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keywords = "Autoencoder, Convolutional neural network, Deep learning, Deep belief network, Restricted Boltzmann machine"
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}
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MISC
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@misc{openData,
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title={Open Database License (ODbL) v1.0},
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url={https://opendatacommons.org/licenses/odbl/1.0/},
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journal={Open Data Commons},
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year={2018},
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month={Feb}
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}
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@incollection{NIPS2012_4824,
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title = {ImageNet Classification with Deep Convolutional Neural Networks},
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author = {Alex Krizhevsky and Sutskever, Ilya and Hinton, Geoffrey E},
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booktitle = {Advances in Neural Information Processing Systems 25},
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editor = {F. Pereira and C. J. C. Burges and L. Bottou and K. Q. Weinberger},
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pages = {1097--1105},
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year = {2012},
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publisher = {Curran Associates, Inc.},
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url = {http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf}
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}
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@ARTICLE{726791,
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author={Y. Lecun and L. Bottou and Y. Bengio and P. Haffner},
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journal={Proceedings of the IEEE},
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title={Gradient-based learning applied to document recognition},
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year={1998},
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volume={86},
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number={11},
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pages={2278-2324},
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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},
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doi={10.1109/5.726791},
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ISSN={0018-9219},
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month={Nov},}
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@book{numpy,
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title={A guide to NumPy},
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author={Oliphant, Travis E},
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volume={1},
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year={2006},
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publisher={Trelgol Publishing USA}
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}
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@article{scikit-learn,
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title={Scikit-learn: Machine Learning in {P}ython},
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author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
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and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
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and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
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Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
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journal={Journal of Machine Learning Research},
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volume={12},
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pages={2825--2830},
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year={2011}
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}
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@misc{kaggle,
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title = {Kaggle: The Home of Data Science \& Machine Learning},
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howpublished = {\url{https://www.kaggle.com/}},
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note = {Accessed: 2018-05-25}
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}
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