# Changeset 56

Ignore:
Timestamp:
Mar 18, 2004, 10:12:24 PM (18 years ago)
Message:

adding a choice not to train on all the samples

Location:
trunk/src
Files:
2 edited

### Legend:

Unmodified
 r55 /// /// Training the SVM using the SMO algorithm. Input is the C-parameter, /// which is converted to \f$D=1/C\f$, since \f$1/C\f$ is what is used in /// the algorithm. Default should correspond to maximal margin /// (\f$C=\inf\f$), but since I don't trust double inf I use default /// \f$C=0\f$ and turn that into \f$D=0\f$. /// Training the SVM using the a modified version of the SMO /// algorithm. Input is the C-parameter, which is converted to /// \f$D=1/C\f$, since \f$1/C\f$ is what is used in the algorithm. Default /// should correspond to maximal margin (\f$C=\inf\f$), but since I don't /// trust double inf I use default \f$C=0\f$ and turn that into /// \f$D=0\f$. The second input defines what samples that is included in /// the training, e.g. [1,2,3] means train on samples 1, 2 and 3. /// void train(const double = 0); void train(const double = 0, const gslapi::vector& = gslapi::vector(1,0)); /// /// Function will return \f$\alpha\f$