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svm-struct使用指南（原版翻译）
svm-struct官方网站：http://www.cs.cornell.edu/people/tj/svm_light/svm_struct.html
个人翻译的svm_struct_learn文件，翻译的有些粗糙，望不要介意！希望对你有帮助！
<span style="font-size:18px;">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.
<span style="white-space:pre"> </span>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
}</span><span style="font-size:18px;">
-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 function exp(-gamma ||a-b||^2)
3: sigmoid tanh(s a*b + c)
4: user defined kernel from kernel.h
核函数类型：
0：线性（默认）
1：多项式 (s a*b+c)^d
2：RBF核函数 exp(-gamma ||a-b||^2)
3：sigmoid核函数 (s a*b + c)
4：自定义核函数 kernel.h
-d int -> parameter d in polynomial kernel
多项式核函数中参数d
-g float -> parameter gamma in rbf kernel
RBF核中的gamma参数
-s float -> parameter s in sigmoid/poly kernel
sigmoid/poly（多项式）核中的s参数
-r float -> parameter c in sigmoid/poly kernel
sigmoid/poly（多项式）核中的c参数
-u string -> parameter of user defined kernel
用户自定义核
Output Options::
-a string -> write all alphas to this file after learning
(in the same order as in the training set)
学习完成后，写入所有alphas到文件（与训练集顺序相同）
References::
[1] T. Joachims, Learning to Align Sequences: A Maximum Margin Approach.
Technical Report, September, 2003.</span><span style="font-size:18px;"> [2] I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun, Large Margin
Methods for Structured and Interdependent Output Variables, Journal
of Machine Learning Research (JMLR), Vol. 6(Sep):1453-1484, 2005.
[3] T. Joachims, Making Large-Scale SVM Learning Practical. Advances in
Kernel Methods - Support Vector Learning, B. Schölkopf and C. Burges and
A. Smola (ed.), MIT Press, 1999.
[4] T. Joachims, Learning to Classify Text Using Support Vector
Machines: Methods, Theory, and Algorithms. Dissertation, Kluwer,
2002.
[5] T. Joachims, T. Finley, Chun-Nam Yu, Cutting-Plane Training of Structural
SVMs, Machine Learning Journal, to appear.
[6] <a target=_blank href=http://www.mamicode.com/"http://svmlight.joachims.org/">http://svmlight.joachims.org/svm-struct使用指南（原版翻译）
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