references.md

Sebastian Raschka, 2015
Python Machine Learning

References & Resources

A list of references as they appear throughout the chapters.

A BibTeX version for your favorite reference manager is available here.

Chapter 1: Machine Learning - Giving Computers the Ability to Learn from Data 

Literature

• F. Galton. Regression towards mediocrity in hereditary stature. Journal of the Anthropological Institute of Great Britain and Ireland, pages 246–263, 1886.

Links

• Python: https://www.python.org

• Installing Python: https://docs.python.org/3/installing/index.html

• Anaconda Scientific Python Distribution: https://store.continuum.io/cshop/anaconda/

Additional Resources & Further Reading

• Python Tutorial

Chapter 2: Training Simple Machine Learning Algorithms for Classification

Literature

• W. S. McCulloch and W. Pitts. A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5(4):115–133, 1943.

• F. Rosenblatt. The perceptron, a perceiving and recognizing automaton Project Para. Cornell Aeronautical Laboratory, 1957.

• B. Widrow. Adaptive ”Adaline” neuron using chemical ”memistors”. Number Technical Report 1553-2. Stanford Electron. Labs., Stanford, CA, October 1960.

Links

• NumPy Tutorial: http://wiki.scipy.org/Tentative_NumPy_Tutorial

• Pandas Tutorial: http://pandas.pydata.org/pandas-docs/stable/tutorials.html

• Matplotlib Tutorial: http://matplotlib.org/users/beginner.html

• IPython Notebook: https://ipython.org/ipython-doc/3/notebook/index.html

• BLAS (Basic Linear Algebra Subprograms): http://www.netlib.org/blas/

• LAPACK — Linear Algebra PACKage: http://www.netlib.org/lapack/

• UCI Machine Learning Repository: http://archive.ics.uci.edu/ml/

• Iris dataset: https://archive.ics.uci.edu/ml/datasets/Iris

Additional Resources & Further Reading

• Z. Kolter. Linear algebra review and reference, 2008.

Chapter 3: A Tour of Advanced Machine Learning Classifiers Using Scikit-Learn

Literature

• D. H. Wolpert and W. G. Macready. No free lunch theorems for optimization. Evolutionary Computation, IEEE Transactions on, 1(1):67–82, 1997.

• D. H. Wolpert. The supervised learning no-free-lunch theorems. In Soft Computing and Industry, pages 25–42. Springer, 2002.

• S. Menard. Logistic regression: From introductory to advanced concepts and applications. Sage Publica- tions, 2009.

• V. Vapnik. The nature of statistical learning theory. Springer Science & Business Media, 2013.

• C. J. Burges. A tutorial on support vector machines for pattern recognition. Data mining and knowledge discovery, 2(2):121–167, 1998.

• J. H. Friedman, J. L. Bentley, and R. A. Finkel. An algorithm for finding best matches in logarithmic expected time. ACM Transactions on Mathematical Software (TOMS), 3(3):209–226, 1977.

Links

• scikit-learn: http://scikit-learn.org/stable/

• LIBLINEAR -- A Library for Large Linear Classification: http://www.csie.ntu.edu.tw/~cjlin/liblinear/

• LIBSVM -- A Library for Support Vector Machines https://www.csie.ntu.edu.tw/~cjlin/libsvm/

• Graphviz - Graph Visualization Software: http://www.graphviz.org

Additional Resources & Further Reading

• L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone. Classification and regression trees. wadsworth. Belmont, CA, 1984.

• L. Breiman. Random forests. Machine learning, 45(1):5–32, 2001.

• P. Cunningham and S. J. Delany. k-nearest neighbour classifiers. Multiple Classifier Systems, pages 1–17, 2007.

Chapter 4: Building Good Training Sets – Data Pre-Processing 

Literature

• T. Hastie, J. Friedman, and R. Tibshirani. The Elements of Statistical Learning, volume 2. Springer, 2009. Section 3.4.

• F. Ferri, P. Pudil, M. Hatef, and J. Kittler. Comparative study of techniques for large-scale feature selection. Pattern Recognition in Practice IV, pages 403–413, 1994.

Links

• Wine Data Set: https://archive.ics.uci.edu/ml/datasets/Wine

Additional Resources & Further Reading

• One-hot encoding: https://en.wikipedia.org/wiki/One-hot

• M. Y. Park and T. Hastie. L1-regularization path algorithm for generalized linear models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 69(4):659–677, 2007.

• A. Y. Ng. Feature selection, L1 vs. L2 regularization, and rotational invariance. In Proceedings of the twenty-first international conference on Machine learning, page 78. ACM, 2004.

• D. W. Aha and R. L. Bankert. A comparative evaluation of sequential feature selection algorithms. In Learning from Data, pages 199–206. Springer, 1996.

• C. Strobl, A.-L. Boulesteix, A. Zeileis, and T. Hothorn. Bias in random forest variable importance measures: Illustrations, sources and a solution. BMC bioinformatics, 8(1):25, 2007.

Chapter 5: Compressing Data via Different Dimensionality Reduction Techniques

Literature

• R. A. Fisher. The use of multiple measurements in taxonomic problems. Annals of eugenics, 7(2):179–188, 1936.

• C. R. Rao. The utilization of multiple measurements in problems of biological classification. Journal of the Royal Statistical Society. Series B (Methodological), 10(2):159–203, 1948.

• A. M. Martinez and A. C. Kak. PCA versus LDA. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 23(2):228–233, 2001.

• R. O. Duda, P. E. Hart, and D. G. Stork. Pattern classification. 2nd. Edition. New York, 2001.

• B. Schoelkopf, A. Smola, and K.-R. Mueller. Kernel principal component analysis. pages 583–588, 1997.

Links

Additional Resources & Further Reading

• I. Jolliffe. Principal component analysis. Wiley Online Library, 2002.

• Manifold learning algorithms: https://en.wikipedia.org/wiki/Nonlinear_dimensionality_reduction#Manifold_learning_algorithms

• J. Shawe-Taylor and N. Cristianini. Kernel methods for pattern analysis. Cambridge university press, 2004.

Chapter 6: Learning Best Practices for Model Evaluation and Hyperparameter Optimization 

Literature

• R. Kohavi et al. A study of cross-validation and bootstrap for accuracy estimation and model selection. In Ijcai, volume 14, pages 1137–1145, 1995.

• M. Markatou, H. Tian, S. Biswas, and G. M. Hripcsak. Analysis of variance of cross-validation estimators of the generalization error. Journal of Machine Learning Research, 6:1127–1168, 2005.

• B. Efron and R. Tibshirani. Improvements on cross-validation: the 632+ bootstrap method. Journal of the American Statistical Association, 92(438):548–560, 1997.

• S. Varma and R. Simon. Bias in error estimation when using cross-validation for model selection. BMC bioinformatics, 7(1):91, 2006.

Links

• Breast Cancer Wisconsin dataset: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)

Additional Resources & Further Reading

• Y. Bengio and Y. Grandvalet. No unbiased estimator of the variance of k-fold cross-validation. The Journal of Machine Learning Research, 5:1089–1105, 2004.

• S. Raschka. An overview of general performance metrics of binary classifier systems. Computing Research Repository (CoRR), abs/1410.5330, 2014.

• J. A. Hanley and B. J. McNeil. The meaning and use of the area under a receiver operating characteristic (roc) curve. Radiology, 143(1):29–36, 1982.

• J. Davis and M. Goadrich. The relationship between precision-recall and roc curves. In Proceedings of the 23rd international conference on Machine learning, pages 233–240. ACM, 2006.

• J. Bergstra and Y. Bengio. Random search for hyper-parameter optimization. The Journal of Machine Learning Research, 13(1):281–305, 2012.

Chapter 7: Combining Different Models for Ensemble Learning

Literature

• D. H. Wolpert. Stacked generalization. Neural networks, 5(2):241–259, 1992.

• L. Breiman. Bagging predictors. Machine learning, 24(2):123–140, 1996.

• R. E. Schapire. The strength of weak learnability. Machine learning, 5(2):197–227, 1990.

• Y. Freund, R. E. Schapire, et al. Experiments with a new boosting algorithm. In ICML, volume 96, pages 148–156, 1996.

• L. Breiman. Bias, variance, and arcing classifiers. 1996.

• G. Raetsch, T. Onoda, and K. R. Mueller. An improvement of adaboost to avoid overfitting. In Proc. of the Int. Conf. on Neural Information Processing. Citeseer, 1998.

• A. Toescher, M. Jahrer, and R. M. Bell. The bigchaos solution to the netflix grand prize. Netflix prize documentation, 2009.

Links

• Netflix Recommendations: Beyond the 5 stars (Part 1): http://techblog.netflix.com/2012/04/netflix-recommendations-beyond-5-stars.html

Additional Resources & Further Reading

• K. M. Ting and I. H. Witten. Issues in stacked generalization. J. Artif. Intell. Res.(JAIR), 10:271–289, 1999.

• J. H. Friedman. Stochastic gradient boosting. Computational Statistics & Data Analysis, 38(4):367–378, 2002.

Chapter 8: Applying Machine Learning to Sentiment Analysis 

Literature

• A. L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts. Learning word vectors for sentiment analysis. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 142–150, Portland, Oregon, USA, June 2011. Association for Computational Linguistics.

• I. Kanaris, K. Kanaris, I. Houvardas, and E. Stamatatos. Words versus character n-grams for anti-spam filtering. International Journal on Artificial Intelligence Tools, 16(06):1047–1067, 2007.

• S. Raschka. Naive bayes and text classification I - introduction and theory. Computing Research Repos- itory (CoRR), abs/1410.5329, 2014.

• S. Bird, E. Klein, and E. Loper. Natural language processing with Python. O’Reilly Media, Inc.”, 2009.

• M. F. Porter. An algorithm for suffix stripping. Program: electronic library and information systems, 14(3):130–137, 1980.

• M. Toman, R. Tesar, and K. Jezek. Influence of word normalization on text classification. Proceedings of InSciT, pages 354–358, 2006.

• A. Appleby. murmurhash3, 2011.

• T. Mikolov, K. Chen, G. Corrado, and J. Dean. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781, 2013.

Links

• Review dataset: http://ai.stanford.edu/~amaas/data/sentiment/

• Google regex Tutorial: https://developers.google.com/edu/python/regular-expressions

• Natural Language Toolkit: http://www.nltk.org

• Google Word2Vec: https://code.google.com/p/word2vec/

Additional Resources & Further Reading

• A. Aizawa. An information-theoretic perspective of tf–idf measures. Information Processing & Manage- ment, 39(1):45–65, 2003.

• M. F. Porter. Snowball: A language for stemming algorithms, 2001.

• C. D. Paice. Method for evaluation of stemming algorithms based on error counting. Journal of the American Society for Information Science, 47(8):632–649, 1996.

Chapter 9: Embedding a Machine Learning Model into a Web Application 

Literature

Links

• Flask: http://flask.pocoo.org

• SQLite: http://www.sqlite.org

• SQLite Manager Add-on: https://addons.mozilla.org/en-US/firefox/addon/sqlite-manager/

• WTForms: https://wtforms.readthedocs.org/en/latest/

• Jinja2: http://jinja.pocoo.org

• Webapp example: http://raschkas.pythonanywhere.com

• pythonanywhere: https://www.pythonanywhere.com

Additional Resources & Further Reading

• HTTP Methods: GET vs. POST: http://www.w3schools.com/tags/ref_httpmethods.asp

Chapter 10: Predicting Continuous Target Variables with Regression Analysis 

Literature

• A. I. Khuri. Introduction to linear regression analysis, by Douglas C. Montgomery, Elizabeth A. Peck, G. Geoffrey Vining. International Statistical Review, 81(2):318–319, 2013.

• D. S. G. Pollock. The Classical Linear Regression Model.

• R. Toldo and A. Fusiello. Automatic estimation of the inlier threshold in robust multiple structures fitting. In Image Analysis and Processing–ICIAP 2009, pages 123–131. Springer, 2009.

Links

• Housing dataset: https://archive.ics.uci.edu/ml/datasets/Housing

• Seaborn: http://stanford.edu/~mwaskom/software/seaborn/

Additional Resources & Further Reading

• J. W. Tukey. Exploratory data analysis. 1977.

• I. Lawrence and K. Lin. A concordance correlation coefficient to evaluate reproducibility. Biometrics, pages 255–268, 1989.

• N. J. Nagelkerke. A note on a general definition of the coefficient of determination. Biometrika, 78(3):691– 692, 1991.

• P. Meer, D. Mintz, A. Rosenfeld, and D. Y. Kim. Robust regression methods for computer vision: A review. International journal of computer vision, 6(1):59–70, 1991.

• A. E. Hoerl and R. W. Kennard. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics, 12(1):55–67, 1970.

• R. Tibshirani. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B (Methodological), pages 267–288, 1996.

• A. Liaw and M. Wiener. Classification and regression by randomForest. R news, 2(3):18–22, 2002.

• G. Louppe. Understanding random forests: From theory to practice. arXiv preprint arXiv:1407.7502, 2014.

• G. Louppe, L. Wehenkel, A. Sutera, and P. Geurts. Understanding variable importances in forests of randomized trees. In Advances in Neural Information Processing Systems, pages 431–439, 2013.

• S. R. Gunn et al. Support vector machines for classification and regression. ISIS technical report, 14, 1998.

Chapter 11: Working with Unlabeled Data – Clustering Analysis  

Literature

• D. Arthur and S. Vassilvitskii. k-means++: The advantages of careful seeding. In Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, pages 1027–1035. Society for Industrial and Applied Mathematics, 2007.

• J. C. Dunn. A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters. 1973.

• J. C. Bezdek. Pattern recognition with fuzzy objective function algorithms. Springer Science & Business Media, 2013.

• S. Ghosh and S. K. Dubey. Comparative analysis of k-means and fuzzy c-means algorithms. IJACSA, 4:35–38, 2013.

• M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In Kdd, volume 96, pages 226–231, 1996.

Links

Additional Resources & Further Reading

• Z. Huang. Extensions to the k-means algorithm for clustering large data sets with categorical values. Data mining and knowledge discovery, 2(3):283–304, 1998.

• C. Ding and X. He. K-means clustering via principal component analysis. In Proceedings of the twenty- first international conference on Machine learning, page 29. ACM, 2004.

• Y. Ding, Y. Zhao, X. Shen, M. Musuvathi, and T. Mytkowicz. Yinyang k-means: A drop-in replacement of the classic k-means with consistent speedup. In Proceedings of the 32nd International Conference on Machine Learning (ICML-15), pages 579–587, 2015.

• P. J. Rousseeuw. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 20:53–65, 1987.

• J.-P. Rasson and T. Kubushishi. The gap test: an optimal method for determining the number of natural classes in cluster analysis. In New approaches in classification and data analysis, pages 186–193. Springer, 1994.

• S. C. Johnson. Hierarchical clustering schemes. Psychometrika, 32(3):241–254, 1967.

Chapter 12: Training Artificial Neural Networks for Image Recognition

Literature

• D. R. G. H. R. Williams and G. Hinton. Learning representations by back-propagating errors. Nature, pages 323–533, 1986.

• Y. Taigman, M. Yang, M. Ranzato, and L. Wolf. Deepface: Closing the gap to human-level performance in face verification. In Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, pages 1701–1708. IEEE, 2014.

• A. Hannun, C. Case, J. Casper, B. Catanzaro, G. Diamos, E. Elsen, R. Prenger, S. Satheesh, S. Sengupta, A. Coates, et al. Deepspeech: Scaling up end-to-end speech recognition. arXiv preprint arXiv:1412.5567, 2014.

• T. Unterthiner, A. Mayr, G. Klambauer, and S. Hochreiter. Toxicity prediction using deep learning. arXiv preprint arXiv:1503.01445, 2015.

• T. Hastie, J. Friedman, and R. Tibshirani. The Elements of Statistical Learning, volume 2. Springer, 2009.

• Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324, 1998.

• A. G. Baydin and B. A. Pearlmutter. Automatic differentiation of algorithms for machine learning. arXiv preprint arXiv:1404.7456, 2014.

• Y. Bengio. Learning deep architectures for AI. Foundations and trends in Machine Learning, 2(1):1–127, 2009.

• P. Y. Simard, D. Steinkraus, and J. C. Platt. Best practices for convolutional neural networks applied to visual document analysis. In null, page 958. IEEE, 2003.

• S. Hochreiter and J. Schmidhuber. Long short-term memory. Neural computation, 9(8):1735–1780, 1997.

• C. M. Bishop. Neural networks for pattern recognition. Oxford university press, 1995.

Links

• "How Google Translate squeezes deep learning onto a phone": http://googleresearch.blogspot.com/2015/07/how-google-translate-squeezes-deep.html

• Article about Endianness: https://en.wikipedia.org/wiki/Endianness

Additional Resources & Further Reading

• Automatic differentiation: https://en.wikipedia.org/wiki/Automatic_differentiation

Chapter 13: Parallelizing Neural Network Training with Theano

Literature

• J. Bergstra, O. Breuleux, F. Bastien, P. Lamblin, R. Pascanu, G. Desjardins, J. Turian, D. Warde-Farley, and Y. Bengio. Theano: A cpu and gpu math compiler in python. In Proc. 9th Python in Science Conf, pages 1–7, 2010.

Links

• LISA Lab: http://lisa.iro.umontreal.ca

• Theano: http://deeplearning.net/software/theano/

• Pylearn2: http://deeplearning.net/software/pylearn2/

• Lasagne: https://lasagne.readthedocs.org/en/latest/

• Keras: http://keras.io

• SymPy: http://www.sympy.org/en/index.html

People

• Geoff Hinton http://www.cs.toronto.edu/~hinton/,

• Andrew Ng http://www.andrewng.org

• Yann LeCun http://yann.lecun.com

• Juergen Schmidhuber http://people.idsia.ch/~juergen/

• Yoshua Bengio http://www.iro.umontreal.ca/~bengioy

Additional Resources & Further Reading

• Symbolic Computation: https://en.wikipedia.org/wiki/Symbolic_computation

• What Every Programmer Should Know About Floating-Point Arithmetic: http://floating-point-gui.de