I finally finished integration of LibSVM in my software. Trying to reproduce Joachims' results on reuters and ohsumed-23 I got the following on the micro-averaged breakeven point:
- On reuters: 84.2 (Joachims), 85.9 (me).
- On ohsumed: 60.7 (Joachims), 64.8 (me).
The differences can be due to the difference on the stopword list (I used the famous 571 words of the SMART system which is almost a standard) and my own processing procedure (I remove all punctuation marks). Indeed the results are really good, but the great difference in ohsumed is mysterious...
By the way, training time in my Core 2 Duo 2Ghz, for LibSVM is 4m28s, and classification 1m42s. It is the Java version, but it is still affordable. Who said SVMs were slow?
On the following days, I will try to improve my k-NN implementation (at this time, it has no inverted index, and so is terrifyingly slow), and to include another Bayesian network classifier (Sahami's "limited dependence bayesian classifier"), which I think could be improved in some way to make it competitive with SVMs.
By the way, training time in my Core 2 Duo 2Ghz, for LibSVM is 4m28s, and classification 1m42s. It is the Java version, but it is still affordable. Who said SVMs were slow?
On the following days, I will try to improve my k-NN implementation (at this time, it has no inverted index, and so is terrifyingly slow), and to include another Bayesian network classifier (Sahami's "limited dependence bayesian classifier"), which I think could be improved in some way to make it competitive with SVMs.
