Guys, I found the great library for SVM (Support Vector Machine) while I browse via Internet on Universität Freiburg - Institut für Informatik where my university friends from Electrical Engineering ITS studies. I think it's a good idea from your offering to try apply PhD at Universität Freiburg - Institut für Informatik. Let me upgrade my
Deutsch language...hehehe 
. Back to SVM, using this library we can do a research about artificial intelligent for classification and pattern recognition. This library is called "LIBSVMTL". If you don't familiar with SVM concept , I recommend you to read these two books ( I have these books, please contact me if you need to read these books):
- Support Vector Machines: Theory and Applications written by Prof. Lipo Wang from Nanyang Technological University, Singapore.
- Support Vector Machines for Pattern Classification , written by Prof. Shigeo Abe from Kobe University, Japan.
Those books are explained us about fundamental of SVM based on mathematics & statistics analysis and algorithm (pusing..pusing deh bacanya 
).
LIBSVMTL is a highly customizable C++ Support Vector Machine library based on Chih-Jen Lin's LIBSVM. The goals of splitting the library into template classes were:
- Let user decide wether to use sparse or dense storage for feature vectors
- Easier extention with new algorithms via a more object oriented design,
e.g. in libsvmTL one kernel (which is the kernel function, its name,
commandline parameters, loading/saving its parameters, description of
commandline parameters) is represented by one object. Same for
two-class and multi-class algorithms
- Integration into own programs (e.g., using own FeatureVector classes, using your own data file format, etc.)
- Easier development and maintainance of interfaces to higher level languages (e.g. matlab, python, etc)
- Making the library parts as independent as possible, to allow multiple developers to contribute
- speed improvements
Besides that, the low-level training algorithms and structure is as identical to original libsvm from Chih-Jen-Lin as possible (only some minor changes to include the templated feature vectors and kernel functions), which allows easy integration of further improvements in libsvm and ensures correct numerics.
You can download this library here [V]