Organise references

mini_proj/report/references.bib

@@ -1,9 +1,11 @@
-@misc{openData,
+Classical Machine Learning
-  title={Open Database License (ODbL) v1.0},
+@article{MLReview,
-  url={https://opendatacommons.org/licenses/odbl/1.0/},
+  title={Supervised machine learning: A review of classification techniques},
-  journal={Open Data Commons},
+  author={Kotsiantis, Sotiris B and Zaharakis, I and Pintelas, P},
-  year={2018},
+  journal={Emerging artificial intelligence applications in computer engineering},
-  month={Feb}
+  volume={160},
+  pages={3--24},
+  year={2007}
 }
 @techreport{knn,
   title={Discriminatory analysis-nonparametric discrimination: consistency properties},
@@ -48,21 +50,52 @@
   pages={18--22},
   year={2002}
 }
-@article{Kotsiantis2007,
+
-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.},
+Neural Networks
-author = {Kotsiantis, Sotiris B.},
+@article{lenet,
-doi = {10.1115/1.1559160},
+  title={Gradient-based learning applied to document recognition},
-file = {:home/kelvin/.local/share/data/Mendeley Ltd./Mendeley Desktop/Downloaded/Kotsiantis - 2007 - Supervised machine learning A review of classification techniques.pdf:pdf},
+  author={LeCun, Yann and Bottou, L{\'e}on and Bengio, Yoshua and Haffner, Patrick},
-isbn = {1586037803},
+  journal={Proceedings of the IEEE},
-issn = {09226389},
+  volume={86},
-journal = {Informatica},
+  number={11},
-keywords = {algorithms analysis classifiers computational conn,classifiers,data mining techniques,intelligent data analysis,learning algorithms},
+  pages={2278--2324},
-mendeley-groups = {CS Proj/ML,CS Proj,Thesis,Thesis/ML},
+  year={1998},
-pages = {249--268},
+  publisher={IEEE}
-title = {{Supervised machine learning: A review of classification techniques}},
+}
-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},
+@inproceedings{alexnet,
-volume = {31},
+  title={Imagenet classification with deep convolutional neural networks},
-year = {2007}
+  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},

mini_proj/report/waldo.tex

@@ -101,12 +101,10 @@
 
 		\todo{
 		\\A couple of papers that may be useful (if needed):
-		- LeNet: http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf
+		- LeNet: \cite{lenet}
-		- AlexNet: http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks
+		- AlexNet: \cite{alexnet}
-		- General comparison of LeNet and AlexNet:
+		- General comparison of LeNet and AlexNet:\cite{lenetVSalexnet}
-		"On the Performance of GoogLeNet and AlexNet Applied to Sketches", Pedro Ballester and Ricardo Matsumura Araujo
+		- Deep NN Architecture:\cite{deepNN}
-		- Deep NN Architecture:
-		https://www-sciencedirect-com.ezproxy.lib.monash.edu.au/science/article/pii/S0925231216315533
 		}
 
 		\subsection{Classical Machine Learning Methods}
@@ -114,7 +112,7 @@
 		The following paragraphs will give only brief descriptions of the different
 		classical machine learning methods used in this reports. For further reading
 		we recommend reading ``Supervised machine learning: A review of
-		classification techniques'' \cite{Kotsiantis2007}.
+		classification techniques'' \cite{MLReview}.
 
 		\paragraph{Naive Bayes Classifier}