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Courses in Summer term 2008 / Semi-supervised Learning:

Time: Wednesday 14-16
Vorbesprechung: 09.04
Begin: 09.04

Semi-supervised learning is a class of machine learning techniques that make use of both labeled and unlabeled data for training - typically a small amount of labeled data with a large amount of unlabeled data. Semi-supervised learning falls between unsupervised learning (without any labeled training data) and supervised learning (with completely labeled training data). Many machine-learning researchers have found that unlabeled data, when used in conjunction with a small amount of labeled data, can produce considerable improvement in learning accuracy. The acquisition of labeled data for a learning problem often requires a skilled human agent to manually classify training examples. The cost associated with the labeling process thus may render a fully labeled training set infeasible, whereas acquisition of unlabeled data is relatively inexpensive. In such situations, semi-supervised learning can be of great practical value. Semi-supervised learning methods have been applied in different domains, for instance in text and web mining, speech recognition and bioinformatics.

The seminar aims at presenting a broad overview of different semi-supervised learning methods. The seminar is based on the book Chapelle, Schölkopf, and Zien (eds.): "Semi-Supervised Learning" and on selected papers. Knowledge in statistics or machine learning could be useful (but are not formaly required).

Talks can be given in English or German.

Supervisor: Christine Preisach



  1. Introduction to Supervised Learning
  2. Introduction to Unsupervised Learning
  3. Generative Models

  4. Co-Training Algorithm
  5. Kernel Methods and Fisher Kernel
  6. Semi-Supervised Text Classification Using EM
  7. Risks of Semi-Supervised Learning
  8. Probabilistic Semi-Supervised Clustering with Constraints
  9. Low-Density Separation

  10. Transductive Support Vector Machines
  11. Gaussian Processes and the Null-Category Noise Model
  12. Graph-Based Methods

  13. Label Propagation in Graphs
  14. Semi-Supervised Learning with Conditional Harmonic Mixing
  15. Change of Representation

  16. Spectral Methods for Dimensionality Reduction
  17. Semi-Supervised Learning in Practice

  18. Semi-Supervised Protein Classification Using Cluster Kernels
  19. Prediction of Protein Function from Networks

Interested students can register for a topic from now via email to .