Question

We need to decide between Support Vector Machines and Fast Artificial Neural Network for some text processing project.

It includes Contextual Spelling Correction and then tagging the text to certain phrases and their synonyms.

Which will be the right approach? Or is there an alternate to both of these… Something more appropriate than FANN as well as SVM?

Answer 1

I think you’ll get a competitive results from both of the algorithms, so you should aggregate the results… think about ensemble learning.

Update:
I don’t know if this is specific enough: use Bayes Optimal Classifier to combine the prediction from each algorithm. You have to train both of your algorithms, then you have to train the Bayes Optimal Classifier to use your algorithms and make optimal predictions based on the input of the algorithms.

Separate your training data in 3:

• 1st data set will be used to train the (Artificial) Neural Network and the Support Vector Machines.
• 2nd data set will be used to train the Bayes Optimal Classifier by taking the raw predictions from the ANN and SVM.
• 3rd data set will be your qualification data set where you will test your trained Bayes Optimal Classifier.

Update 2.0:
Another way to create an ensemble of the algorithms is to use 10-fold (or more generally, k-fold) cross-validation:

• Break data into 10 sets of size n/10.
• Train on 9 datasets and test on 1.
• Repeat 10 times and take a mean accuracy.

Remember that you can generally combine many the classifiers and validation methods in order to produce better results. It’s just a matter of finding what works best for your domain.