56 template<
typename WrappedSVM>
62 template<
typename FV>
75 _svm =
new WrappedSVM;
97 return std::string(
"plattprob_") + WrappedSVM::name();
102 return std::string(
"Platts Probability estimates for ")
103 + WrappedSVM::description();
119 template<
typename FV>
141 template<
typename ForwardIter>
142 void train( ForwardIter FV_begin,
const ForwardIter& FV_end,
143 typename Traits<
typename std::iterator_traits<ForwardIter>::value_type>::
ModelType& model)
const 172 template<
typename ForwardIter,
typename Accessor>
173 void train( ForwardIter FV_begin,
const ForwardIter& FV_end,
175 Accessor accessor)
const;
189 template<
typename FV>
190 double classify(
const FV& testObject,
202 #include "PlattProb.icc" The GroupedTrainingData class is a container for feature vectors.
svt::PlattProbModel< typename WrappedSVM::Traits< FV >::ModelType > ModelType
void train(ForwardIter FV_begin, const ForwardIter &FV_end, typename Traits< typename std::iterator_traits< ForwardIter >::value_type >::ModelType &model) const
train (STL-like interface) Your feature vector class must provide a method double getLabel()...
void train(const GroupedTrainingData< FV > &trainData, typename Traits< FV >::ModelType &model) const
train SVM with given Feature Vectors.
PlattProb(WrappedSVM *svm=0)
double classify(const FV &testObject, const typename Traits< FV >::ModelType &model) const
standard classification without caching the kernel evaluations.
static std::string name()
static std::string description()