I've posted a new article on CodeProject, entitled “Handwriting Recognition Revisited: Kernel Support Vector Machines”. It is a continuation of a previous article on handwritten digits recognition but this time using SVMs instead of KDA.
The code uses the SVM library in Accord.NET Framework. The framework, which runs on .NET and is written mostly in C#, supports standard or multiclass support vector machines for either classification or regression, having more than 20 kernel functions available to choose from.
In the article, the same set and the same amount of training and testing samples have been used as in the previous Kernel Discriminant Analysis article for comparison purposes. The experimental results shows that SVMs can outperform KDA both in terms of efficiency and accuracy, requiring much less processing time and memory available while still producing robust results.
