/usr/include/shogun/latent/LatentSVM.h is in libshogun-dev 3.2.0-7.5.
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* This program is free software; you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation; either version 3 of the License, or
* (at your option) any later version.
*
* Written (W) 2012 Viktor Gal
* Copyright (C) 2012 Viktor Gal
*/
#ifndef __LATENTSVM_H__
#define __LATENTSVM_H__
#include <shogun/lib/common.h>
#include <shogun/machine/LinearLatentMachine.h>
namespace shogun
{
/** @brief LatentSVM class
* Latent SVM implementation based on [1].
* For optimization this implementation uses SVMOcas.
*
* User must provide a her own CLatentModel which implements the PSI(x_i,h_i)
* function for the given problem.
*
* [1] P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan,
* "Object detection with discriminatively trained part-based models,"
* Pattern Analysis and Machine Intelligence,
* IEEE Transactions on, vol. 32, no. 9, pp. 1627-1645, 2010.
*
*/
class CLatentSVM: public CLinearLatentMachine
{
public:
/** default contstructor */
CLatentSVM();
/** constructor
*
* @param model the user defined CLatentModel object.
* @param C regularization constant
*/
CLatentSVM(CLatentModel* model, float64_t C);
virtual ~CLatentSVM();
/** apply linear machine to all examples
*
* @return resulting labels
*/
virtual CLatentLabels* apply_latent();
using CLinearLatentMachine::apply_latent;
/** Returns the name of the SGSerializable instance.
*
* @return name of the SGSerializable
*/
virtual const char* get_name() const { return "LatentSVM"; }
protected:
/** inner loop of the latent machine
*
* The optimization part after finding the argmax_h for the
* positive examples in the outter loop. It uses SVMOcas for
* finding the cutting plane.
*
* @param cooling_eps epsilon
* @return primal objective value
*/
virtual float64_t do_inner_loop(float64_t cooling_eps);
};
}
#endif /* __LATENTSVM_H__ */
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