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Networks for Learning: Regression and Classification >> Content Detail



Assignments



Assignments

  1. Two problem sets.
  2. There will be a term paper for graduate credit due on the last day of class. The paper should either present some new work addressing a relevant research problem, or provide a critical analysis of 2-3 papers on some aspect of learning, approximation, or networks. A short description of the project you intend to work on is due before Lec #18. Below is a list of available topics; other topics must be approved by the instructor.

    List of available projects:

    Project 1:   Hypothesis Testing with Small Sets.
    Project 2:   Connection between MED and Regularization.
    Project 3:   Kernels for Strings.
    Project 4:   Feature Selection for SVMs: Theory and Experiments.
    Project 5:   Morphable Models and Roweis' Nonlinear Dimensionality Reduction.
    Project 6:   Optimal Bayes Classification Rule and SVMs: Estimation of ROC Curves.
    Project 7:   IOHMMs: Evaluation of HMMs vs Direct Classifiers like SVMs.
    Project 8:   Reusing the Test Set: Datamining Bounds.
    Project 9:   Stability and Generalization.
    Project 10: Learning with Very Large Dataset.
    Project 11: Bagging, Boosting, and Stability.
    Project 12: Local vs. Global Classifiers: Simulations and Use of Prior Knowledge.
    Project 13: Invariance to Measure of the RKHS Norm in the Continuum.
    Project 14: Concentration Experiments: Dot Products vs Distances in Very High Dimension.


 



 








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