1. A Comparative Study of Kernel Methods for Classification Applications
Yan Liu
Sep 23, 2003

2. Introduction Support Vector Machines Various kernels
Text classification
Protein classification
Various kernels
Standard kernels
Linear kernels, polynomial kernels, RBF kernels
Other application-oriented kernels
Fisher-kernels, String kernels and etc

3. Problem Definition
There has been little study focusing on the behaviors of different kernels for:
Rare-class problem (unbalanced data)
Noisy data problem
Multi-label problem
These problems are common in the real applications:
Text classification
Protein Family classification

4. Text Classification Kernel selection Problem Focus Dataset
Linear kernels
String kernels
Problem Focus
Rare-class problem
Multi-class problem
Dataset
Reuters21578 dataset

5. Protein Family Classification
Kernel selection
Linear kernels
String kernels
Fisher-kernels
Problem Focus
Rare-class problem
Noisy data problem
Dataset
GPCR classification dataset

6. Methodology and Schedule
Propose conjectures on the possible behaviors according to analysis
Sep 12th ~ Sep 28th
Work on synthetic datasets to testify hypothesis
Sep 28th ~ Oct 20th
Map from synthetic data to real application data
Oct 20th ~ Sep 18th

7. Mid-course Deliverables
Analysis of the dataset
Class distribution (rare-class and multi-class)
Noise level
Conjectures for possible behaviors
Results on synthetic datasets
Explanation and interesting observations from the results

8. Multi-label Problem for Text Classification
Related work
Binary classification (one-vs-all) (by Yang; Joachims)
Mixture Model by EM (by McCallum)
Rank-based approach
Boosting (by Schapire & Singer)
Rank-based kernels (by Elsseeff & Weston)

9. Multi-label Problem for Text Classification
Possible Solutions
Combine Mixture Model and Kernel-based approach using Fisher-kernels
Similar idea as using HMM and SVM together for protein classification