Machine learning refined: Foundations, algorithms, and applications

Machine Learning Refined: Foundations, Algorithms, and Applications

Volumn , Issue , 2016, Pages 1-286

  Watt, Jeremy ^a   Borhani, Reza ^a   Katsaggelos, Aggelos K ^b  

^a NORTHWESTERN UNIVERSITY   (United States)

^b Northwestern University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 85022205772     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1017/CBO9781316402276     Document Type: Book

References (81)

1. Gabriella Csurka et al. Visual categorization with bags of keypoints.Workshop on Statistical Learning in Computer Vision, ECCV, volume 1, no. 1-22, 2004.
2. Jianguo Zhang et al. Local features and kernels for classification of texture and object categories: A comprehensive study. International Journal of Computer Vision 73(2) 213-238, 2007.
3. David G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2) 91-110, 2004.
4. Svetlana Lazebnik, Cordelia Schmid, and Jean Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on, volume 2. IEEE, 2006.
5. Jianchao Yang et al. Linear spatial pyramid matching using sparse coding for image classification. Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. IEEE, 2009.
6. Geoffrey Hinton et al. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. Signal Processing Magazine, IEEE 29(6) 82-97, 2012.
7. Yoshua Bengio, Ian Goodfellow, and Aaron Courville. Deep learning. An MIT Press book in preparation. Draft chapters available at http://www.iro.umontreal.ca/~bengioy/dlbook (2014).
8. Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv: 1409.1556(2014).
9. Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. Deep learning. Nature 521(7553) 436-444, 2015.
10. Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems. 2012.
11. Bernhard Schölkopf and Alexander J. Smola. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, 2002.
12. World economic outlook database, https://www.imf.org/external/pubs/ft/weo/2013/02/weodata/index.aspx.
13. Anelia Angelova, Yaser Abu-Mostafa, and Pietro Perona. Pruning training sets for learning of object categories. In Computer Vision and Pattern Recoanition, 2005. CVPR 2005. IEEE Computer Society Conference on, volume 1, pp. 494-501. IEEE, 2005.
14. Sitaram Asur and Bernardo A Huberman. Predicting the future with social media. In IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010, volume 1, pp. 492-499. IEEE, 2010.
15. Horace Barlow. Redundancy reduction revisited. Network: Computation in Neural Systems, 12(3) 241-253, 2001.
16. Horace B Barlow. The coding of sensory messages. In Current Problems in Animal Behaviour, pp. 331-360, 1961.
17. Yoshua Bengio, Yann LeCun, et al. Scaling learning algorithms towards AI. Large-scale Kernel Machines, 34(5), 2007.
18. Dimitri P Bertsekas. Incremental gradient, subgradient, and proximal methods for convex optimization: A survey. In Optimization for Machine Learning, 2010, 1-38, MIT Press, 2011.
19. Christopher M Bishop. Neural Networks for Pattern Recognition. Oxford University Press, 1995.
20. Christopher M Bishop et al. Pattern Recognition and Machine Learning, volume 4. Springer, 2006.
21. Léon Bottou. Large-scale machine learning with stochastic grant descent. In Proceedings of COMPSTAT’2010, pp. 177-186. Springer, 2010.
22. Léon Bottou and Chih-Jen Lin. Support vector machine solvers. Large Scale Kernel Machines, pp. 301-320, MIT Press, 2007.
23. Stephen Boyd, Neal Parikh, Eric Chu, Borja Peleato, and Jonathan Eckstein. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends ®in Machine Learning, 3(1) 1-122, 2011.
24. Stephen Poythress Boyd and Lieven Vandenberghe. Convex Optimization. Cambridge University Press, 2004.
25. Hilton Bristow and Simon Lucey. Why do linear svms trained on hog features perform so well? arXiv preprint arXiv: 1406.2419, 2014.
26. Paul R Burton, David G Clayton, Lon R Cardon, et al. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature, 447(7145) 661-678, 2007.
27. Olivier Chapelle. Training a support vector machine in the primal. Neural Computation, 19(5) 1155-1178, 2007.
28. George Cybenko. Approximation by superpositions of a sigmoidal function. Mathematics of Control, Signals and Systems, 2(4) 303-314, 1989.
29. Navneet Dalal and Bill Triggs. Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, volume 1, pp. 886-893. IEEE, 2005.
30. Richard O Duda, Peter E Hart, and David G Stork. Pattern Classification. John Wiley & Sons, 2012.
31. Jeremy Elson, John R Douceur, Jon Howell, and Jared Saul. Asirra: a captcha that exploits interest-aligned manual image categorization. In ACM Conference on Computer and Communications Security, pp. 366-374. Citeseer, 2007.
32. Markus Enzweiler and Dariu M Gavrila. Monocular pedestrian detection: Survey and experiments. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 31(12) 2179-2195, 2009.
33. Carmen Fernandez, Eduardo Ley, and Mark FJ Steel. Model uncertainty in cross-country growth regressions. Journal of Applied Econometrics, 16(5) 563-576, 2001.
34. Jerome Friedman, Trevor Hastie, Robert Tibshirani, et al. Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). The Annals of Statistics, 28(2) 337-407, 2000.
35. Galileo Galilei. Dialogues Concerning Two New Sciences. Dover, 1914.
36. Xavier Glorot, Antoine Bordes, and Yoshua Bengio. Deep sparse rectifier networks. In Proceedings of the 14th International Conference on Artificial Intelligence and Statistics. JMLR W&CP Volume, volume 15, pp. 315-323, 2011.
37. James Douglas Hamilton. Time Series Analysis, volume 2. Princeton University Press, 1994.
38. Kurt Hornik, Maxwell Stinchcombe, and Halbert White. Multilayer feedforward networks are universal approximators. Neural Networks, 2(5) 359-366, 1989.
39. Dilawar (http://math.stackexchange.com/users/1674/dilawar). Largest eigenvalue of a positive semi-definite matrix is less than or equal to sum of eigenvalues of its diagonal blocks. Mathematics Stack Exchange. URL: http://math.stackexchange.com/q/144890 (version: 2012-05-14).
40. Xuedong Huang, Alex Acero, Hsiao-Wuen Hon, et al. Spoken Language Processing, volume 18. Prentice Hall, 2001.
41. Judson P Jones and Larry A Palmer. An evaluation of the two-dimensional gabor filter model of simple receptive fields in cat striate cortex. Journal of Neurophysiology, 58(6) 1233-1258, 1987.
42. Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, pp. 1097-1105, NIPS, 2012.
43. Yann LeCun and Yoshua Bengio. Convolutional networks for images, speech, and time series. The Handbook of Brain Theory and Neural Networks, 3361(10), MIT Press, 1995.
44. Yann LeCun, Koray Kavukcuoglu, and Clément Farabet. Convolutional networks and applications in vision. In Circuits and Systems (ISCAS), Proceedings of 2010 IEEE International Symposium on, pp. 253-256. IEEE, 2010.
45. Daniel D Lee and H Sebastian Seung. Algorithms for non-negative matrix factorization. In Advances in Neural Information Processing Systems, pp. 556-562, MIT Press, 2001.
46. Donghoon Lee, Wilbert Van der Klaauw, Andrew Haughwout, Meta Brown, and Joelle Scally. Measuring student debt and its performance. FRB of New York Staff Report, (668), 2014.
47. Moshe Lichman. UCI Machine Learning Repository, [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science, 2013.
48. Jianqiang Lin, Sang-Mok Lee, Ho-Joon Lee, and Yoon-Mo Koo. Modeling of typical microbial cell growth in batch culture. Biotechnology and Bioprocess Engineering, 5(5) 382-385, 2000.
49. Zhiyun Lu, Avner May, Kuan Liu, et al. How to scale up kernel methods to be as good as deep neural nets. arXiv preprint arXiv: 1411.4000, 2014.
50. David G Luenberger. Linear and Nonlinear Programming. Springer, 2003.
51. David J C MacKay. Introduction to gaussian processes. NATO ASI Series F Computer and Systems Sciences, 168 133-166, 1998.
52. David J C MacKay. Information Theory, Inference and Learning Algorithms. Cambridge University Press, 2003.
53. Saturnino Maldonado-Bascon, Sergio Lafuente-Arroyo, Pedro Gil-Jimenez, Hilario Gomez-Moreno, and Francisco López-Ferreras. Road-sign detection and recognition based on support vector machines. Intelligent Transportation Systems, IEEE Transactions on, 8(2): 264-278, 2007.
54. Christopher D Manning and Hinrich Schütze. Foundations of Statistical Natural Language Processing. MIT Press, 1999.
55. Stjepan Marčelja. Mathematical description of the responses of simple cortical cells. JOSA, 70(11) 1297-1300, 1980.
56. Valerii Mayer and Ekaterina Varaksina. Modern analogue of ohm’s historical experiment. Physics Education, 49(6) 689, 2014.
57. Gordon E Moore. Cramming more components onto integrated circuits. Proceedings of the IEEE, 86(1): 82-85, 1998.
58. Isaac Newton. The Principia: Mathematical Principles of Natural Philosophy. University of California Press, 1999.
59. Jorge Nocedal and S Wright. Numerical Optimization, Series in Operations Research and Financial Engineering. Springer-Verlag, 2006.
60. Bruno A Olshausen and David J Field. Sparse coding with an overcomplete basis set: A strategy employed by v1? Vision Research, 37(23) 3311-3325, 1997.
61. Brad Osgood. The Fourier transform and its applications. Electrical Engineering Department, Stanford University, 2009.
62. Reggie Panaligan and Andrea Chen. Quantifying movie magic with google search. Google Whitepaper-Industry Perspectives+ User Insights, 2013.
63. Jooyoung Park and IrwinWSandberg. Universal approximation using radial-basis-function networks. Neural Computation, 3(2) 246-257, 1991.
64. Jeffrey Pennington, Felix Yu, and Sanjiv Kumar. Spherical random features for polynomial kernels. In Advances in Neural Inforamtion Processing Systems, pages 1837-1845, NIPS, 2015.
65. Simon J D Prince. Computer Vision: Models, Learning, and Inference. Cambridge University Press, 2012.
66. Ning Qian. On the momentum term in gradient descent learning algorithms. Neural Networks, 12(1) 145-151, 1999.
67. Lawrence R Rabiner and Biing-Hwang Juang. Fundamentals of Speech Recognition, volume 14, Prentice-Hall, 1993.
68. Ali Rahimi and Benjamin Recht. Random features for large-scale kernel machines. In Advances in Neural Inforamtion Processing Systems, pp. 1177-1184, NIPS, 2007.
69. Ali Rahimi and Benjamin Recht. Uniform approximation of functions with random bases. In Communication, Control, and Computing, 2008 46th Annual Allerton Conference on, pp. 555-561. IEEE, 2008.
70. Ryan Rifkin and Aldebaro Klautau. In defense of one-vs-all classification. The Journal of Machine Learning Research, 5 101-141, 2004.
71. Walter Rudin. Principles of Mathematical Analysis, volume 3. McGraw-Hill, 1964.
72. Xavier X Sala-i Martin. I just ran two million regressions. The American Economic Review, pp. 178-183, 1997.
73. Jonathan Shewchuk. An introduction to the conjugate gradient method without the agonizing pain, http://www-2.cs.cmu.edu/jrs/jrspapers, 1994.
74. Elias M Stein and Rami Shakarchi. Fourier Analysis: An Introduction, volume 1. Princeton University Press, 2011.
75. Samuele Straulino. Reconstruction of Galileo Galilei’s experiment: the inclined plane. Physics Education, 43(3) 316, 2008.
76. Silke Szymczak, Joanna M Biernacka, Heather J Cordell, et al. Machine learning in genome-wide association studies. Genetic Epidemiology, 33(S1) S51-S57, 2009.
77. Yichuan Tang. Deep learning using linear support vector machines. arXiv preprint arXiv: 1306.0239, 2013.
78. Andrea Vedaldi and Brian Fulkerson. Vlfeat: An open and portable library of computer vision algorithms. In Proceedings of the International Conference on Multimedia, pp. 1469-1472. ACM, 2010.
79. Pierre Verhulst. Notice sur la loi que la population poursuit dans son accroissement. Correspondance Mathématique et Physique 10: 113-121. Technical report, Retrieved 09/08, 2009.
80. Patrik Waldmann, Gábor Mészáros, Birgit Gredler, Christian Fürst, and Johann Sölkner. Evaluation of the lasso and the elastic net in genome-wide association studies. Frontiers in Genetics, 4, 2013.
81. Horn, Roger A and Johnson, Charles R. Matrix analysis. Cambridge University Press, 2012.