svm-struct-matlab-1.2-mex版本

SVM_STRUCT_LEARN Calls the SVM-struct solver MODEL = SVM_STRUCT_LEARN(ARGS, PARM) runs SVM-struct solver with parameters ARGS on the problem PARM. See [1-6] for the theory. SPARM is a structure of with the fields 运行SVM-struct通过参数ARGS解决PARM问题。看参考文献[1-6]理解原理。 SPARM的构成如下 PATTERNS:: patterns (X) A cell array of patterns. The entries can have any nature (they can just be indexes of the actual data for example). 一种单元阵列，输入可以有任何性质的 LABELS:: labels (Y) A cell array of labels. The entries can have any nature. LOSSFN:: loss function callback A handle to the loss function. This function has the form L = LOSS(PARAM, Y, YBAR) where PARAM is the SPARM structure, Y a ground truth label, YBAR another label, and L a non-negative scalar. 损失函数操作，形式为L = LOSS(PARAM, Y, YBAR)，PARAM是SAPRM结构， Y是分类标签（在机器学习中，术语“ground truth”指的是用于有监督训 练的训练集的分类准确性。），YBAR为其他标签。L是一个非负标量。 CONSTRAINTFN:: constraint callback A handle to the constraint generation function. This function has the form YBAR = FUNC(PARAM, MODEL, X, Y) where PARAM is the input PARM structure, MODEL is the a structure representing the current model, X is an input pattern, and Y is its ground truth label. YBAR is the most violated labels. 约束生成函数操作，形式为YBAR = FUNC(PARAM, MODEL, X, Y)， PARAM参数输入为PARM参数，MODEL代表当前模型，X是需要输入的数据，Y是分类标签。 YBAR是 FEATUREN:: feature map callback A handle to the feature map. This function has the form PSI = FEATURE(PARAM, X, Y) where PARAM is the input PARM structure, X is a pattern, Y is a label, and PSI a sparse vector of dimension PARM.DIMENSION. This handle does not need to be specified if kernels are used. 特征图谱操作。形式为PSI = FEATURE(PARAM, X, Y)，PARAM参数输入为PARM参数， X是数据，Y是标签，PSI是PARM.DIMENSION维数构成的稀疏向量。 如果引用kernels核函数，该操作不需要被定义。 DIMENSION:: dimension of the feature map The dimension of the feature map. This value does not need to be specified if kernels are used. 特征图谱的维数，如果引用kernels核函数，该操作不需要被定义。 KERNELFN:: kernel function callback A handle to the kernel function. This function has the form K = KERN(PARAM, X, Y, XP, YP) where PARAM is the input PARM structure, and X, Y and XP, YP are two pattern-label pairs, input of the joint kernel. This handle does not need to be specified if feature maps are used. 核函数操作，形式为K = KERN(PARAM, X, Y, XP, YP)，PARAM参数输入为PARM参数， X, Y和XP, YP是两个数据-标签对，输入共同的内核。 如果已用了特征图谱则不需要定义此函数操作。 MODEL is a structure with fields: W:: weight vector This is a spare vector of size PARAM.DIMENSION. It is used with feature maps. 权向量。由PARAM.DIMENSION尺寸构成的稀疏向量。用于特征图谱。 ALPHA:: dual variables 对偶变量。 SVPATTERNS:: patterns which are support vectors 支持向量数 SVLABELS:: labels which are support vectors 支持向量标签 Used with kernels. 用于核函数。 ARGS is a string specifying options in the usual struct SVM. These are: ARGS是用于普通struct SVM的常规字符命令。如下： General Options:: -v [0..3] -> verbosity level (default 1) 详细等级（默认1） -y [0..3] -> verbosity level for svm_light (default 0) Learning Options:: -c float -> C: trade-off between training error and margin (default 0.01) C:训练误差与边界的权衡 -p [1,2] -> L-norm to use for slack variables. Use 1 for L1-norm, use 2 for squared slacks. (default 1) L范数用于松弛变量。1用于L1范数，2用于平方松弛。 { 当p取1，2，∞的时候分别是以下几种最简单的情形： 1-范数：║x║1=│x1│+│x2│+…+│xn│ 2-范数：║x║2=sqrt(│x1│^2+│x2│^2+…+│xn│^2) ∞-范数：║x║∞=max（│x1│，│x2│，…，│xn│） 原本公式为:║x║p=(│x1│^p+│x2│^p+…+│xn│^p)^1/p } -o [1,2] -> Rescaling method to use for loss. 1: slack rescaling 2: margin rescaling 损失的调节模式： 1.松弛调节 2.边界调节 -l [0..] -> Loss function to use. 0: zero/one loss ?: see below in application specific options 损失函数使用： 0: 0/1 损失 ？：看下面应用的特定选项 Optimization Options (see [2][5]):: -w [0,..,9] -> choice of structural learning algorithm 0: n-slack algorithm described in [2] 1: n-slack algorithm with shrinking heuristic 2: 1-slack algorithm (primal) described in [5] 3: 1-slack algorithm (dual) described in [5] 4: 1-slack algorithm (dual) with constraint cache [5] 9: custom algorithm in svm_struct_learn_custom.c 选择结构化学习算法： 0：n-slack算法在[2]中描述过 1：n-slack算法通过收缩式启发（也不知道该如何翻译）实现 2：1-slack算法（primal）在[5]中描述过 3：1-slack算法（dual）在[5]中描述过 4：1-slack算法（dual）通过约束缓存[5] 9：自定义算法，通过svm_struct_learn_custom.c -e float -> epsilon: allow that tolerance for termination criterion epsilon（希腊字母）：终止条件阈值 -k [1..] -> number of new constraints to accumulate before recomputing the QP solution (default 100) (-w 0 and 1 only) 验证QP方案前新的约束条件数（默认100）（只在-w 0和1） -f [5..] -> number of constraints to cache for each example (default 5) (used with -w 4) 缓存每个样例的约束数（-w 4时使用） -b [1..100] -> percentage of training set for which to refresh cache when no epsilon violated constraint can be constructed from current cache (default 100) (used with -w 4) 通过当前缓存构造没有epsilon违反约束的训练集的百分数（-w 4时使用） SVM-light Options for Solving QP Subproblems (see [3]):: -n [2..q] -> number of new variables entering the working set in each svm-light iteration (default n = q). Set n < q to prevent zig-zagging. 每一次svm-light迭代输入工作集的新变量数（默认n=q）。 设置n<q阻止“曲折的”（或翻译为反复的，不知道该如何表达） -m [5..] -> size of svm-light cache for kernel evaluations in MB (default 40) (used only for -w 1 with kernels) 核评估中svm-light缓存的大小（MB）（默认40）（仅在-w 1时使用） -h [5..] -> number of svm-light iterations a variable needs to be optimal before considered for shrinking (default 100) 考虑收缩前svm-light迭代一个变量为最优化的次数 -# int -> terminate svm-light QP subproblem optimization, if no progress after this number of iterations. (default 100000) 如果迭代次数超过这个数还没有进展，终止svm-light QP子问题优化 Kernel Options:: -t int -> type of kernel function: 0: linear (default) 1: polynomial (s a*b+c)^d 2: radial basis fu