shark::McSvmLLWTrainer< InputType, CacheType > Class Template Reference

Training of the multi-category SVM by Lee, Lin and Wahba (LLW). More...

#include <shark/Algorithms/Trainers/McSvmLLWTrainer.h>

+ Inheritance diagram for shark::McSvmLLWTrainer< InputType, CacheType >:

Public Types

typedef CacheType QpFloatType
 
typedef AbstractModel< InputType, RealVector > ModelType
 
typedef AbstractKernelFunction< InputTypeKernelType
 
- Public Types inherited from shark::AbstractSvmTrainer< InputType, unsigned int >
typedef AbstractKernelFunction< InputTypeKernelType
 
- Public Types inherited from shark::AbstractTrainer< Model, LabelTypeT >
typedef Model ModelType
 
typedef ModelType::InputType InputType
 
typedef LabelTypeT LabelType
 
typedef LabeledData< InputType, LabelTypeDatasetType
 

Public Member Functions

 McSvmLLWTrainer (KernelType *kernel, double C, bool unconstrained=false)
 
std::string name () const
 From INameable: return the class name. More...
 
void train (KernelClassifier< InputType > &svm, const LabeledData< InputType, unsigned int > &dataset)
 
- Public Member Functions inherited from shark::AbstractSvmTrainer< InputType, unsigned int >
 AbstractSvmTrainer (KernelType *kernel, double C, bool offset, bool unconstrained=false)
 
 AbstractSvmTrainer (KernelType *kernel, double negativeC, double positiveC, bool offset, bool unconstrained=false)
 
double C () const
 Return the value of the regularization parameter C. More...
 
RealVector const & regularizationParameters () const
 
RealVector & regularizationParameters ()
 
KernelTypekernel ()
 
const KernelTypekernel () const
 
void setKernel (KernelType *kernel)
 
bool isUnconstrained () const
 
bool trainOffset () const
 
double CacheSize () const
 
void setCacheSize (std::size_t size)
 
RealVector parameterVector () const
 get the hyper-parameter vector More...
 
void setParameterVector (RealVector const &newParameters)
 set the vector of hyper-parameters More...
 
size_t numberOfParameters () const
 return the number of hyper-parameters More...
 
- Public Member Functions inherited from shark::AbstractTrainer< Model, LabelTypeT >
virtual void train (ModelType &model, DatasetType const &dataset)=0
 Core of the Trainer interface. More...
 
- Public Member Functions inherited from shark::INameable
virtual ~INameable ()
 
- Public Member Functions inherited from shark::ISerializable
virtual ~ISerializable ()
 Virtual d'tor. More...
 
virtual void read (InArchive &archive)
 Read the component from the supplied archive. More...
 
virtual void write (OutArchive &archive) const
 Write the component to the supplied archive. More...
 
void load (InArchive &archive, unsigned int version)
 Versioned loading of components, calls read(...). More...
 
void save (OutArchive &archive, unsigned int version) const
 Versioned storing of components, calls write(...). More...
 
 BOOST_SERIALIZATION_SPLIT_MEMBER ()
 
- Public Member Functions inherited from shark::QpConfig
 QpConfig (bool precomputedFlag=false, bool sparsifyFlag=true)
 Constructor. More...
 
QpStoppingConditionstoppingCondition ()
 Read/write access to the stopping condition. More...
 
QpStoppingCondition const & stoppingCondition () const
 Read access to the stopping condition. More...
 
QpSolutionPropertiessolutionProperties ()
 Access to the solution properties. More...
 
bool & precomputeKernel ()
 Flag for using a precomputed kernel matrix. More...
 
bool const & precomputeKernel () const
 Flag for using a precomputed kernel matrix. More...
 
bool & sparsify ()
 Flag for sparsifying the model after training. More...
 
bool const & sparsify () const
 Flag for sparsifying the model after training. More...
 
bool & shrinking ()
 Flag for shrinking in the decomposition solver. More...
 
bool const & shrinking () const
 Flag for shrinking in the decomposition solver. More...
 
bool & s2do ()
 Flag for S2DO (instead of SMO) More...
 
bool const & s2do () const
 Flag for S2DO (instead of SMO) More...
 
unsigned int & verbosity ()
 Verbosity level of the solver. More...
 
unsigned int const & verbosity () const
 Verbosity level of the solver. More...
 
unsigned long long const & accessCount () const
 Number of kernel accesses. More...
 
void setMinAccuracy (double a)
 
void setMaxIterations (unsigned long long i)
 
void setTargetValue (double v)
 
void setMaxSeconds (double s)
 
- Public Member Functions inherited from shark::IParameterizable
virtual ~IParameterizable ()
 

Additional Inherited Members

- Protected Attributes inherited from shark::AbstractSvmTrainer< InputType, unsigned int >
KernelTypem_kernel
 
RealVector m_regularizers
 Vector of regularization parameters. More...
 
bool m_trainOffset
 
bool m_unconstrained
 Is log(C) stored internally as a parameter instead of C? If yes, then we get rid of the constraint C > 0 on the level of the parameter interface. More...
 
std::size_t m_cacheSize
 Number of values in the kernel cache. The size of the cache in bytes is the size of one entry (4 for float, 8 for double) times this number. More...
 
- Protected Attributes inherited from shark::QpConfig
QpStoppingCondition m_stoppingcondition
 conditions for when to stop the QP solver More...
 
QpSolutionProperties m_solutionproperties
 properties of the approximate solution found by the solver More...
 
bool m_precomputedKernelMatrix
 should the solver use a precomputed kernel matrix? More...
 
bool m_sparsify
 should the trainer sparsify the model after training? More...
 
bool m_shrinking
 should shrinking be used? More...
 
bool m_s2do
 should S2DO be used instead of SMO? More...
 
unsigned int m_verbosity
 verbosity level (currently unused) More...
 
unsigned long long m_accessCount
 kernel access count More...
 

Detailed Description

template<class InputType, class CacheType = float>
class shark::McSvmLLWTrainer< InputType, CacheType >

Training of the multi-category SVM by Lee, Lin and Wahba (LLW).

This is a special support vector machine variant for classification of more than two classes. Given are data tuples \( (x_i, y_i) \) with x-component denoting input and y-component denoting the label 1, ..., d (see the tutorial on label conventions; the implementation uses values 0 to d-1), a kernel function k(x, x') and a regularization constant C > 0. Let H denote the kernel induced reproducing kernel Hilbert space of k, and let \( \phi \) denote the corresponding feature map. Then the SVM classifier is the function

\[ h(x) = \arg \max (f_c(x)) \]

\[ f_c(x) = \langle w_c, \phi(x) \rangle + b_c \]

\[ f = (f_1, \dots, f_d) \]

with class-wise coefficients w_c and b_c given by the (primal) optimization problem

\[ \min \frac{1}{2} \sum_c \|w_c\|^2 + C \sum_i L(y_i, f(x_i)) \]

\[ \text{s.t. } \sum_c f_c = 0 \]

The special property of the so-called LLW-machine is its loss function, which arises from the application of the discriminative sum operator to absolute margin violations. Let \( h(m) = \max\{0, 1-m\} \) denote the hinge loss as a function of the margin m, then the LLW loss is given by

\[ L(y, f(x)) = \sum_{c \not= y} h(-f_c(x)) \]

For more details refer to the paper:

Multicategory Support Vector Machines: Theory and Application to the Classification of Microarray Data and Satellite Radiance Data. Y. Lee, Y. Lin, and G. Wahba. Journal of the American Statistical Association 99(465), 2004.

Definition at line 96 of file McSvmLLWTrainer.h.

Member Typedef Documentation

§ KernelType

template<class InputType, class CacheType = float>
typedef AbstractKernelFunction<InputType> shark::McSvmLLWTrainer< InputType, CacheType >::KernelType

Definition at line 103 of file McSvmLLWTrainer.h.

§ ModelType

template<class InputType, class CacheType = float>
typedef AbstractModel<InputType, RealVector> shark::McSvmLLWTrainer< InputType, CacheType >::ModelType

Definition at line 102 of file McSvmLLWTrainer.h.

§ QpFloatType

template<class InputType, class CacheType = float>
typedef CacheType shark::McSvmLLWTrainer< InputType, CacheType >::QpFloatType

Definition at line 100 of file McSvmLLWTrainer.h.

Constructor & Destructor Documentation

§ McSvmLLWTrainer()

template<class InputType, class CacheType = float>
shark::McSvmLLWTrainer< InputType, CacheType >::McSvmLLWTrainer ( KernelType kernel,
double  C,
bool  unconstrained = false 
)
inline

Constructor

Parameters
kernelkernel function to use for training and prediction
Cregularization parameter - always the 'true' value of C, even when unconstrained is set
unconstrainedwhen a C-value is given via setParameter, should it be piped through the exp-function before using it in the solver?

Definition at line 110 of file McSvmLLWTrainer.h.

Member Function Documentation

§ name()

template<class InputType, class CacheType = float>
std::string shark::McSvmLLWTrainer< InputType, CacheType >::name ( ) const
inlinevirtual

From INameable: return the class name.

Reimplemented from shark::INameable.

Definition at line 115 of file McSvmLLWTrainer.h.

§ train()


The documentation for this class was generated from the following file: