89 lines
4.9 KiB
BibTeX
89 lines
4.9 KiB
BibTeX
@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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@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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@article{Kotsiantis2007,
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abstract = {Supervised machine learning is the search for algorithms that reason from externally supplied instances to produce general hypotheses, which then make predictions about future instances. In other words, the goal of supervised learning is to build a concise model of the distribution of class labels in terms of predictor features. The resulting classifier is then used to assign class labels to the testing instances where the values of the predictor features are known, but the value of the class label is unknown. This paper describes various supervised machine learning classification techniques. Of course, a single article cannot be a complete review of all supervised machine learning classification algorithms (also known induction classification algorithms), yet we hope that the references cited will cover the major theoretical issues, guiding the researcher in interesting research directions and suggesting possible bias combinations that have yet to be explored.},
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author = {Kotsiantis, Sotiris B.},
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doi = {10.1115/1.1559160},
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file = {:home/kelvin/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Kotsiantis - 2007 - Supervised machine learning A review of classification techniques.pdf:pdf},
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isbn = {1586037803},
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issn = {09226389},
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journal = {Informatica},
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keywords = {algorithms analysis classifiers computational conn,classifiers,data mining techniques,intelligent data analysis,learning algorithms},
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mendeley-groups = {CS Proj/ML,CS Proj,Thesis,Thesis/ML},
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pages = {249--268},
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title = {{Supervised machine learning: A review of classification techniques}},
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url = {http://books.google.com/books?hl=en{\&}lr={\&}id=vLiTXDHr{\_}sYC{\&}oi=fnd{\&}pg=PA3{\&}dq=survey+machine+learning{\&}ots=CVsyuwYHjo{\&}sig=A6wYWvywU8XTc7Dzp8ZdKJaW7rc{\%}5Cnpapers://5e3e5e59-48a2-47c1-b6b1-a778137d3ec1/Paper/p800{\%}5Cnhttp://www.informatica.si/PDF/31-3/11{\_}Kotsiantis - S},
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volume = {31},
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year = {2007}
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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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