MachineLearningLiteratur

Fachgebiet Erweiterte Realität (Prof. Gudrun Klinker)
Research Group for Augmented Reality

Literatur Machine Learning


Seminar Wintersemester 2004/05

Anmerkung: die Literatur in diesem Abschnitt dient nur als Ausgangspunkt, alle Vortragenden sollen selbständig weitere Quellen finden. Dazu können natürlich auch die weiter unten angegebenen Verweise herangezogen werden.

1. Concept Learning and Version Spaces

  • T. Mitchell: Machine learning, McGraw-Hill, 1997.

  • C. Mellish: The description identification problem, Artificial Intelligence, 52(2):151-167, 1991.

2. Online Machine Learning Algorithms

  • A. Blum: Online algorithms in machine learning, in: Fiat and Woeginger (eds.): Online Algorithms: the State of the Art, Springer, 1996.

3. Association Rules and Frequent Itemsets: Offline and Online

3.1 Offline

  • D. Hand, H. Mannila, P. Smyth: Principles of data mining, MIT press, 2001.

  • H. Mannila, H. Toivonen: Levelwise search and borders of theories in knowledge discovery, Data Mining and Knowledge Discovery, 1(3):241-258, 1997.

3.2 Online

  • J.H. Chang, W.S. Lee: Finding recent frequent itemsets adaptively over online data streams, in: Proc. of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, p. 487-492, ACM Press, 2003.

4. Decision Trees: Offline and Online

4.1 Offline

  • T. Mitchell: Machine learning, McGraw-Hill, 1997.

4.2 Online

  • P.E. Utgoff: Incremental induction of decision trees, Machine Learning, 4:161-186, 1989.

  • H. Kargupta, B.-H. Park: Mining decision trees from data streams in a mobile environment, in: Proc. of the 2001 IEEE International Conference on Data Mining, p. 281--288, IEEE Press, 2001.

  • J. Gama, R. Rocha, P. Medas: Accurate decision trees for mining high-speed data streams, in: Proc. of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, p. 523-528, ACM Press, 2003.

5. Rule Learning: Offline and Online

5.1 Offline

  • J. Fürnkranz: Separate-and-conquer rule learning, Artificial Intelligence Review, 13(1):3-54, 1999.

  • I.H. Witten, E. Frank: Data mining: practical machine learning tools and techniques with JAVA implementations, Morgan Kaufmann, 2000.

  • T. Mitchell: Machine learning, McGraw-Hill, 1997.

5.2 Online

  • R.J. Mooney, D. Ourston: A multistrategy approach to theory refinement, in: R.S. Michalski, G. Tecuci (eds.): Machine Learning: A Multistrategy Approach, Vol. IV, p. 141--164, Morgan Kaufmann, 1994.

  • B.L. Richards, R.J. Mooney: Automated refinement of first-Order Horn-clause domain theories, Machine Learning, 19(2):95--131, 1995.

6. Support Vector Machines: Offline and Online

6.1 Offline

  • C.J.C Burges: A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, 2(2):121--167, 1998.

  • K.-R. Müller, S. Mika, G. Rätsch, K. Tsuda, B. Schölkopf: An introduction to kernel-based learning algorithms, IEEE Neural Networks, 12(2):181--201, 2001.

  • S. Kramer, E. Frank, C. Helma: Fragment generation and support vector machines for inducing SARs, SAR and QSAR in Environmental Research, 13(5):509--523, 2002.

6.2 Online

  • Y. Li, P.M. Long: The relaxed online maximum margin algorithm, Machine Learning, 46(1-3):361-387, 2002.

7. Bayesian Learning: Offline and Online

7.1 Offline

  • T. Mitchell: Machine learning, McGraw-Hill, 1997.

  • F. Jensen: An introduction to Bayesian networks, Taylor&Francis, 1996.

7.2 Online

  • R. Chen, K. Sivakumar, H. Kargupta: An approach to online Bayesian learning from multiple data streams, in: Proc. of the PKDD-2001 Workshop on Ubiquitous Data Mining for Mobile and Distributed Environments, 2001.

8. Clustering: Offline and Online

8.1 Offline

  • D. Hand, H. Mannila, P. Smyth: Principles of data mining, MIT press, 2001.

  • T. Hastie, R. Tibshirani, J. Friedman: Elements of statistical learning, Springer, 2001.

8.2 Online

  • P. Domingos, G. Hulten: A general method for scaling up machine learning algorithms and its application to clustering, in: Proc. of the Eighteenth International Conference on Machine Learning (ICML-2001), p. 106--113, Morgan Kaufmann, 2001

Seminar Wintersemester 2003/04

Bayesian Decision Theory

Exponential Family and Maximum Likelihood, Sufficient Statistics

Gaussian Mixture Models and the EM Algorithm

Nonparametric techniques

  • Trevor Hastie, Robert Tibshirani, Jerome Friedman
    Elements of Statistical Learning, Springer, 2001. chapter13
  • Tom Mitchell
    Machine Learning, MacGraw Hill, 1997. chapter 8

Hidden Markov Models

  • Rabiner, L.R.
    A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition;
    Proceedings of the IEEE, Vol.77, Iss.2, Feb 1989; Pages:257-286
  • Rabiner, Juang
    Fundamentals of Speech Recognition chapter 6.1-6.16
  • Richard O. Duda, Peter E. Hart, David G. Stork
    Pattern Classification; chapter 3.10

Clustering

  • Richard O. Duda, Peter E. Hart, David G. Stork
    Pattern Classification; chapter 10.1-10.7
  • Trevor Hastie, Robert Tibshirani, Jerome Friedman
    Elements of Statistical Learning, Springer, 2001. chapter 14.3
  • David Hand, Heikki Mannila, Padhraic Smyth
    Principles of Data Mining, MIT Press, 2001. chapter 9.3-9.6
  • Ian H. Witten, Eibe Frank, Morgan Kaufmann
    Data Mining: Practical Machine Learning Tools and Techniques with JAVA Implementations, chapter 6.6
  • Rabiner, Juang
    Fundamentals of Speech Recognition, chapter 6.4.2, 6.15

Frequent Pattern Discovery

[19] 13, [23]

Neural Networks: Introduction and Single-Layer Networks

[5] 3.1-3.6, [2] 3.1-3.5

Neural Networks: Multi-Layer Networks and Learning Algorithms

[5], [2] 4.x, [14]

Support Vector Machines, Kernels

[3] 9, [13] 1

Inductive Logic Programming, Relational Data Mining

[21] 3, 5

Ensemble Methods: Boosting, Bagging, Stacking

[20] 8.7, 8.8, 9, 10, [18] 7.4 ; [1] 9.1-9.7, [16], [17]

Reinforcement Learning

[22] 13, [24]

OT: More on Spam Filtering

Other References (clean up!)

  1. Pattern Classification; Duda, Hart, Stork:
    http://www.amazon.com/exec/obidos/ASIN/0471056693
  2. Neural Networks for Pattern Classification; Bishop:
    http://www.amazon.com/exec/obidos/ASIN/0198538642
  3. Learning From Data; Cherkasky, Mulin:
    http://www.amazon.com/exec/obidos/tg/detail/-/0471154938
  4. Fundamentals of Speech Recognition; Rabiner, Juang:
    http://www.amazon.com/exec/obidos/tg/detail/-/0130151572
  5. Neural Networks FAQ; comp.ai.neural-nets
    ftp://ftp.sas.com/pub/neural/FAQ.html
  6. A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition; Rabiner, L.R. ; Proceedings of the IEEE, Vol.77, Iss.2, Feb 1989; Pages:257-286
  7. Columbia University Graduate Course Site (Prof. Jebara):
    http://www.cs.columbia.edu/~jebara/6998-01/index.html
  8. ETH Zürich Maschinen Lernen II (Prof. Schiele):
    http://www.vision.ethz.ch/ml/ml.html
  9. Carnegie Mellon University Machine Learning (Prof. Mitchell):
    http://www-2.cs.cmu.edu/afs/cs.cmu.edu/project/theo-3/www/ml.html
  10. Robust Regression and Outlier Detection; Rousseeuw, Leroy:
    http://www.amazon.de/exec/obidos/ASIN/0471852333
  11. Robust Regression Methods for Computer Vision: A Review; Meer, Mintz, Kim, Rosenfeld: International Journal of Computer Vision, Vol.6 No.1 1991
  12. A Gentle Tutorial of the EM Algorithm; Jeff A. Bilmes (Berkeley)
  13. Learning with Kernels; Schölkopf, Smola:
    http://www.amazon.de/exec/obidos/ASIN/0262194759
  14. Laerhoven, K. Van, Aidoo K., Lowette S.,
    Real-time analysis of Data from Many Sensors with Neural Networks.
    Proceedings of the fourth International Symposium on Wearable Computers (ISWC) Zurich, 7-9 October 2001. IEEE Press.
  15. K. Van Laerhoven,
    Combining the self-organizing map and k-means clustering for on-line classification of sensordata.
    In Proceedings of the International Conference on Artificial Neural Networks2001 (ICANN'01), Vienna, 2001.
  16. Eric Bauer and Ron Kohavi,
    An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants.
    Machine Learning, 36(1/2), 105-139
  17. Thomas G. Dietterich,
    Ensemble Methods in Machine Learning.
    Lecture Notes in Computer Science, 1857
  18. Data Mining: Practical Machine Learning Tools and Techniques with JAVA Implementations,
    Ian H. Witten, Eibe Frank, Morgan Kaufmann, 2000.
  19. Principles of Data Mining,
    David Hand, Heikki Mannila, Padhraic Smyth, MIT Press, 2001.
  20. Elements of Statistical Learning,
    Trevor Hastie, Robert Tibshirani, Jerome Friedman, Springer, 2001.
  21. Relational Data Mining,
    Saso Dzeroski, Nada Lavrac, Springer, 2001.
  22. Machine Learning,
    Tom Mitchell, MacGraw Hill, 1997.
  23. Levelwise Search and Borders of Theories in Knowledge Discovery,
    Heikki Mannila, Hannu Toivonen,
    Data Mining and Knowledge Discovery 1(3):241-258, 1997.
  24. Reinforcement Learning: A Survey,
    Leslie Pack Kaelbling, Michael Littman and Adrew W. Morre,
    Journal of Artificial Intelligence Research 4: 237 - 285, 1996.
  25. Spam oder nicht Spam? E-Mail sortieren mit Bayes-Filtern; c't 17/03, Seite 150


Edit | Attach | Refresh | Diffs | More | Revision r1.1 - 20 Sep 2012 - 16:53 - Main.guest

rss.gif © 2001-2004, Lehrstuhl für angewandte Softwaretechnik (2000-2004)   
© 2005 am Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality (seit 2005)