
libsvmTL 
LIBSVM 
Feature Vectors 
 sparse Feature Vector
 dense Feature Vector
 your own Feature vector class (it is just a template
parameter)

 sparse storage Feature Vector

Kernels 
 linear
 radial basis function
 polynomial
 sigmoid
 your own Kernel class (it is just a template
parameter)

 linear
 radial basis function
 polynomial
 sigmoid
 your own kernel by modifying svm.cpp

Oneclass SVM's / Regression 
 (not yet, work in progress) oneclass SVM using a hyper
plane
 (not yet, work in progress) oneclass SVM using a hyper
sphere
 your own Oneclass SVM implementation (it is just a template
parameter)

 epsilonSVR
 nuSVR
 probability estimates for SVR
 oneclass SVM using a hyperplane

Twoclass SVM's 
 CSVC
 nuSVC
 (planned) Probability estimating SVM
 your own (guess what, yeah, it is just a template
parameter)

 CSVC
 nuSVC
 probability estimates for CSVC and nuSVC

Multiclass Algorithms 
 One vs. one
 One vs. rest
 your own (it is just a template parameter)

 One vs. one
 (One vs. rest implementation available in LIBSVM
Tools)

Data storage (training/test data, models, results) 
 ASCII file (dense or sparse storage)
 NetCDF
 std::map based Container (keeps all data in memory)
 (planned) Interface to Matlab "Data Structures"
 your own (it is just a template parameter)


cross validation 
 optimized cross validation (uses chached kernel matrices, just
retrains Twoclass SVM's, whose support vectors belong to left out
feature vectors)
 leaveoneout validation  just use cross validation with
nfold = number of training vectors


grid search 
 integrated optimized grid search. (e.g., reuses cached kernel
matrix from previous grid point, if only nonkernelparameters
changed)
 any parameter (e.g. tolerance of termination criterion, etc)
can be used as grid axis

 grid search via python script, which executes shell command
"svmtrain" for each grid point

Full Kernel Matrix caching (for fast cross validation and grid
search) 
 integrated (via Kernel Wrapper)

 not directly available (you may use the "precomputed kernel
Matrices" extension provided in LIBSVM
Tools)

Feature scaling 
 integrated (via Kernel Wrapper). sScale factors are stored in
the model and will be applied onthefly to test
data)

 via external program "svmscale". Scale factors are stored in
an extra file and must be manually applied to test
data
