CvBoostType

标签：cvboosttype   opencv源码分析   

原文如下：

/*****************************************************************************************                                        Boosting                                        *
\****************************************************************************************/

/*
 * CvBoostType
 *
 * The CvBoostType enumeration specifies the boosting type.
 *
 * Remarks
 *   Four different boosting variants for 2 class classification problems are supported:
 *   Discrete AdaBoost, Real AdaBoost, LogitBoost and Gentle AdaBoost.
 *   The L2 (2 class classification problems) and LK (K class classification problems)
 *   algorithms are close to LogitBoost but more numerically stable than last one.
 *   For regression three different loss functions are supported:
 *   Least square, least absolute deviation and huber loss.
 */
typedef enum CvBoostType
{
    CV_DABCLASS = 0, /* 2 class Discrete AdaBoost           */
    CV_RABCLASS = 1, /* 2 class Real AdaBoost               */
    CV_LBCLASS  = 2, /* 2 class LogitBoost                  */
    CV_GABCLASS = 3, /* 2 class Gentle AdaBoost             */
    CV_L2CLASS  = 4, /* classification (2 class problem)    */
    CV_LKCLASS  = 5, /* classification (K class problem)    */
    CV_LSREG    = 6, /* least squares regression            */
    CV_LADREG   = 7, /* least absolute deviation regression */
    CV_MREG     = 8  /* M-regression (Huber loss)           */
} CvBoostType;

/*****************************************************************************************                                        Boosting                                        *
\****************************************************************************************/

/*
 * CvBoostType
 *
 * 结构体CvBoostType 穷举boosting 类型
 *
 * 注：
 *   共有四种boosting变量，这些变量都支持两分类分类器，分别如下：
 *   Discrete AdaBoost, Real AdaBoost, LogitBoost and Gentle AdaBoost.
 *    L2 (两类分类器) and LK (K 类分类器)算法更接近LogitBoost，但是在数值上比 Gentle AdaBoost更稳定
 *   对于回归来说，支持三种不同的损失函数，如下：
 *   Least square, least absolute deviation and huber loss.
 */
typedef enum CvBoostType
{
    CV_DABCLASS = 0, /* 2 class Discrete AdaBoost           */
    CV_RABCLASS = 1, /* 2 class Real AdaBoost               */
    CV_LBCLASS  = 2, /* 2 class LogitBoost                  */
    CV_GABCLASS = 3, /* 2 class Gentle AdaBoost             */
    CV_L2CLASS  = 4, /* classification (2 class problem)    */
    CV_LKCLASS  = 5, /* classification (K class problem)    */
    CV_LSREG    = 6, /* least squares regression            */
    CV_LADREG   = 7, /* least absolute deviation regression */
    CV_MREG     = 8  /* M-regression (Huber loss)           */
} CvBoostType;

CvBoostType

标签：cvboosttype   opencv源码分析   

原文地址：http://blog.csdn.net/ding977921830/article/details/46774837