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Courses in summer term 2008 / Seminar on Semi-supervised Learning / readings:
abstract
readings

Note on Seminar Paper :

  • Due four weeks after end of term
  • 30 pages at most,
  • 3 hard copies(printed) and 1 soft copy (CD).
    • Note on Grades:
  • Marks will be based on presentation (including answers to questions), seminar paper, and general participation (e.g., asking questions),
  • bonuses for own experiments or implementations. If any, please also include in the CD.

    • List of readings (ee = link to electronic edition; ask me for the other references):

      Introduction
    1. . 18.06 Introduction to Supervised Learning. Speaker: Söhren Kampf
      1. [ee] S. B. Kotsiatis (2006): Supervised Machine Learning: A Review of Classification Techniques Informatica 31.
    2. 25.06 Introduction to Unsupervised Learning. Speaker: Christoph Jäger
      1. [ee] S. B. Kotsiatis, P. E. Pintelas (2004): Recent Advances in Clustering: A Brief Survey WSEAS. Transactions on Information Science and Applications..
      2. [ee] Peter Dayan (1999): Unsupervised Learning The MIT Encyclopedia of the Cognitive Sciences..
      3. Generative Models
    3. Co-Training Algorithm.
      1. [ee] A. Blum, T. Mitchel (1998): Combining Labeled and unlabeled data with Co-Training Conference on computational learning.
    4. Kernel Methods and Fisher Kernel.
      1. [ee] Tommi S. Jaakkola, F.L. Chung, R. Luk , C.M. Ng (1998): Exploiting generative models in discriminative classifiers Proceedings of the 1998 conference on Advances in neural information processing systems II.
    5. 02.07 Semi-Supervised Text Classification Using EM . Speaker:Alexander Hundt
      1. O. Chapelle, B. Schölkopf, A. Zien (2006): Semi-Supervised Text Classification Using EM Semi-Supervised Learning, pp. 33--56.
    6. Risks of Semi-Supervised Learning.
      1. O. Chapelle, B. Schölkopf, A. Zien (2006): Risks of Semi-Supervised Learning Semi-Supervised Learning, pp. 57-71.
    7. Probabilistic Semi-Supervised Clustering with Constraints .
      1. O. Chapelle, B. Schölkopf, A. Zien (2006): Probabilistic Semi-Supervised Clustering with Constraints Semi-Supervised Learning, pp. 73-101.
      8. Low-Density Separation
    8. Transductive Support Vector Machines .
      1. O. Chapelle, B. Schölkopf, A. Zien (2006): Transductive Support Vector Machines Semi-Supervised Learning, pp. 105-116.
    9. Gaussian Processes and the Null-Category Noise Model
      1. O. Chapelle, B. Schölkopf, A. Zien (2006): Gaussian Processes and the Null-Category Noise Model Semi-Supervised Learning, pp. 137-149.
      10. Graph-Based Methods
    10. 09.07 Label Propagation in Graphs Speaker: Alexander Schmehl
      1. O. Chapelle, B. Schölkopf, A. Zien (2006): Label Propagation in Graphs Semi-Supervised Learning, pp. 193-215.
    11. Semi-Supervised Learning with Conditional Harmonic Mixing
      1. O. Chapelle, B. Schölkopf, A. Zien (2006): Semi-Supervised Learning with Conditional Harmonic Mixing Semi-Supervised Learning, pp. 251-273.
      12. Change of Representation
    12. Spectral Methods for Dimensionality Reduction
      1. O. Chapelle, B. Schölkopf, A. Zien (2006): Spectral Methods for Dimensionality Reduction Semi-Supervised Learning, pp. 293-306.
      13. Semi-Supervised Learning in Practice
    13. Semi-Supervised Protein Classification Using Cluster Kernels
      1. O. Chapelle, B. Schölkopf, A. Zien (2006): Semi-Supervised Protein Classification Using Cluster Kernels Semi-Supervised Learning, pp. 343-358.
    14. Prediction of Protein Function from Networks
      1. O. Chapelle, B. Schölkopf, A. Zien (2006): Prediction of Protein Function from Networks Semi-Supervised Learning, pp. 361-376.