Comparison to original libsvm

At the current state, libsvmTL provides the following features:

	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
One-class SVM's / Regression	(not yet, work in progress) one-class SVM using a hyper plane (not yet, work in progress) one-class SVM using a hyper sphere your own One-class SVM implementation (it is just a template parameter)	epsilon-SVR nu-SVR probability estimates for SVR one-class SVM using a hyperplane
Two-class SVM's	C-SVC nu-SVC (planned) Probability estimating SVM your own (guess what, yeah, it is just a template parameter)	C-SVC nu-SVC probability estimates for C-SVC and nu-SVC
Multi-class 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)	Sparse vector format
cross validation	optimized cross validation (uses chached kernel matrices, just retrains Two-class SVM's, whose support vectors belong to left out feature vectors) leave-one-out validation -- just use cross validation with nfold = number of training vectors	basic cross validation,
grid search	integrated optimized grid search. (e.g., reuses cached kernel matrix from previous grid point, if only non-kernel-parameters 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 "svm-train" 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 on-the-fly to test data)	via external program "svm-scale". Scale factors are stored in an extra file and must be manually applied to test data

Missing Features

Some of the features of the Chih-Jen Lin's LIBSVM are not integrated yet. These are:

• Probability estimates (soon)
• Java implemtentaion (not planned)
• GUI for demonstration, like Chih-Jen Lin's svm_toy (maybe sometimes)