Using machine learning for exploratory data analysis and predictive models on large datasets

Study program/ Specialization: Computer Science Spring semester, 2015 Open / Restricted access Writer: Chengwei Xiao ................................................ (Writer’s signature) Faculty supervisor: Dr. Rui Máximo Esteves, Prof. Chunming Rong Title of thesis: Using Machine Learning for Exploratory Data Analysis and Predictive Models on Large Datasets Credits (ECTS): 30

[1]  R. Sakia.  The Box-Cox transformation technique: a review , 1992 .

[2]  H. Abdi.  Partial least squares regression and projection on latent structure regression (PLS Regression) , 2010 .

[3]  Robert P. Sheridan,et al.  Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling , 2003, J. Chem. Inf. Comput. Sci..

[4]  B. Kowalski,et al.  Partial least-squares regression: a tutorial , 1986 .

[5]  J M Gardiner,et al.  Changes in memory awareness during learning: the acquisition of knowledge by psychology undergraduates. , 1997, Journal of experimental psychology. General.

[6]  Kilian Q. Weinberger,et al.  An Introduction to Nonlinear Dimensionality Reduction by Maximum Variance Unfolding , 2006, AAAI.

[7]  H. Abdi,et al.  Principal component analysis , 2010 .

[8]  Ulf Leser,et al.  High-performance information extraction with AliBaba , 2009, EDBT '09.

[9]  Huan Liu,et al.  Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution , 2003, ICML.

[10]  Johannes Fürnkranz,et al.  Incremental Reduced Error Pruning , 1994, ICML.

[11]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[12]  Arthur E. Hoerl,et al.  Ridge Regression: Biased Estimation for Nonorthogonal Problems , 2000, Technometrics.

[13]  James C. Hayton,et al.  Factor Retention Decisions in Exploratory Factor Analysis: a Tutorial on Parallel Analysis , 2004 .

[14]  H. Ney,et al.  Linear discriminant analysis for improved large vocabulary continuous speech recognition , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[15]  Franklin A. Graybill,et al.  Theory and Application of the Linear Model , 1976 .

[16]  Geoffrey E. Hinton,et al.  Unsupervised learning : foundations of neural computation , 1999 .

[17]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[18]  M. Hakan Arslan,et al.  Application of ANN to evaluate effective parameters affecting failure load and displacement of RC buildings , 2009 .

[19]  D. Donoho,et al.  Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[20]  Carla E. Brodley,et al.  Pruning Decision Trees with Misclassification Costs , 1998, ECML.

[21]  John R. Anderson,et al.  MACHINE LEARNING An Artificial Intelligence Approach , 2009 .

[22]  B. Thompson.  Canonical Correlation Analysis , 1984 .

[23]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[24]  Jon Louis Bentley,et al.  Multidimensional binary search trees used for associative searching , 1975, CACM.

[25]  José Manuel Benítez,et al.  Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS , 2012 .

[26]  Ranjan Acharyya,et al.  A New Approach for Blind Source Separation of Convolutive Sources - Wavelet Based Separation Using Shrinkage Function , 2008 .

[27]  Matti Pietikäinen,et al.  Supervised Locally Linear Embedding , 2003, ICANN.

[28]  John Shawe-Taylor,et al.  Canonical Correlation Analysis: An Overview with Application to Learning Methods , 2004, Neural Computation.

[29]  B. Thompson.  Canonical Correlation Analysis: Uses and Interpretation , 1984 .

[30]  Trevor Hastie,et al.  An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.

[31]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[32]  Max Kuhn,et al.  caret: Classification and Regression Training , 2015 .

[33]  Volker Roth,et al.  Nonlinear Discriminant Analysis Using Kernel Functions , 1999, NIPS.

[34]  R. Bro,et al.  Centering and scaling in component analysis , 2003 .

[35]  Vipin Kumar,et al.  Text Categorization Using Weight Adjusted k-Nearest Neighbor Classification , 2001, PAKDD.

[36]  J. Friedman.  Stochastic gradient boosting , 2002 .

[37]  Greg Ridgeway,et al.  Generalized Boosted Models: A guide to the gbm package , 2006 .

[38]  L. Saul,et al.  An Introduction to Locally Linear Embedding , 2001 .

[39]  Jieping Ye,et al.  Two-Dimensional Linear Discriminant Analysis , 2004, NIPS.

[40]  Jaime G. Carbonell,et al.  Introduction: Paradigms for Machine Learning , 1989, Artif. Intell..

[41]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[42]  Pierre Baldi,et al.  Bioinformatics - the machine learning approach (2. ed.) , 2000 .

[43]  Ryszard S. Michalski,et al.  A theory and methodology of inductive learning , 1993 .

[44]  A. Kaplan,et al.  A Beginner's Guide to Partial Least Squares Analysis , 2004 .

[45]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[46]  Hugo de Garis,et al.  The CAM-Brain Machine (CBM): an FPGA-based hardware tool that evolves a 1000 neuron-net circuit module in seconds and updates a 75 million neuron artificial brain for real-time robot control , 2002, Neurocomputing.

[47]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[48]  Ashutosh Kumar Singh,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2010 .

[49]  Ian T. Jolliffe,et al.  Principal Component Analysis , 2002, International Encyclopedia of Statistical Science.

[50]  Olivier Chapelle,et al.  Model Selection for Support Vector Machines , 1999, NIPS.

[51]  Taiwo Oladipupo Ayodele,et al.  Types of Machine Learning Algorithms , 2010 .

[52]  Jason Bell,et al.  Machine Learning: Hands-On for Developers and Technical Professionals , 2014 .

[53]  Babak Saleh,et al.  Toward automated discovery of artistic influence , 2014, Multimedia Tools and Applications.

[54]  Sanmay Das,et al.  Filters, Wrappers and a Boosting-Based Hybrid for Feature Selection , 2001, ICML.

[55]  Geoffrey I. Webb,et al.  # 2001 Kluwer Academic Publishers. Printed in the Netherlands. Machine Learning for User Modeling , 1999 .

[56]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[57]  R. A. van den Berg,et al.  Centering, scaling, and transformations: improving the biological information content of metabolomics data , 2006, BMC Genomics.

[58]  Andrew C. Inkpen.  Learning and knowledge acquisition through international strategic alliances , 1998 .

[59]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .