// -*- C++ -*-#ifndef __LEMGA_AGGREGATING_CASCADE_H__#define __LEMGA_AGGREGATING_CASCADE_H__/** @file *  @brief Declare @link lemga::Cascade Cascade@endlink class * *  $Id: cascade.h 1907 2004-12-11 00:51:14Z ling $ */#include <utility>#include "aggregating.h"namespace lemga {/** @brief Aggregate hypotheses in a cascade (sequential) way. * *  For classification problems, aggregating of hypotheses can be *  done in a cascade way. That is, a list of classifiers are trained *  from the training data; an unknown input is first classified *  using the first hypothesis in the list. If the first hypothesis *  cannot decide the classification with high reliability, the input *  is fed into the next one and so on. (See Cascade::operator() for *  details.) We can have as many hypotheses as the problem demands. * *  The ``reliability'' of a decision is usually determined by a *  concept named @em margin. There exist different definitions in *  literature, such as @f$yf(x)@f$ in %AdaBoost, *  @f$y(w\cdot x-t)/|w|@f$ in SVM. Despite of the differences in *  definitions, higher margins usually implicit more robustness to *  input disturbance and thus better generalization. * *  When margin is used in cascade to decide whether to go on to the *  next hypothesis, the real output @a y is unknown. A natual *  alternative is to use the ``sign'' part of the margin, i.e., *  @f$f(x)@f$ in %AdaBoost and @f$(w\cdot x-t)/|w|@f$ in SVM, and *  take the magnitude as the margin. Similar concepts, such as *  belief in belief propagation and log-likelihood in coding, can *  also be used. * *  We use the name ``belief'' in this class for binary-class problems. *  Very positive and very negative beliefs indicate strong confidence *  in the predicting and thus high reliability. * *  @todo General definition of margin; More explanation of Cascade */class Cascade : public Aggregating {protected:    std::vector<REAL> upper_margin; ///<    std::vector<REAL> lower_margin; ///<public:    virtual Cascade* create () const = 0;    virtual Cascade* clone () const = 0;    /** @todo Unclear about the support of weghted data */    virtual bool support_weighted_data () const { return true; }    virtual REAL train () = 0;    virtual Output operator() (const Input&) const;    /// Belief at a specific pair of input and output    virtual REAL belief (const LearnModel&,                         const Input&, const Output&) const;protected:    virtual bool serialize (std::ostream&, ver_list&) const;    virtual bool unserialize (std::istream&, ver_list&,                              const id_t& = empty_id);};} // namespace lemga#ifdef  __CASCADE_H__#warning "This header file may conflict with another `cascade.h' file."#endif#define __CASCADE_H__#endif