经过千辛万苦终于把blitz和boost库编译好了，下面开始看代码喽~
usage: svm_learn [options] example_file model_file
Arguments:
example_file-> file with training data
model_file -> file to store learned decision rule in
General options:
-? -> this help\n
-v [0..3] -> verbosity level (default 1)
-B [0,1] -> binary input files (default 1)
Learning options:
-z {c,r,p} -> select between classification (c), regression (r),
and preference ranking (p) (default classification)
-c float -> C: trade-off between training error
and margin (default [avg. x*x]^-1)
-w [0..] -> epsilon width of tube for regression
(default 0.1)
-j float -> Cost: cost-factor, by which training errors on
positive examples outweight errors on negative
examples (default 1) (see [4])
-b [0,1] -> use biased hyperplane (i.e. x*w+b>0) instead
of unbiased hyperplane (i.e. x*w>0) (default 1)
-i [0,1] -> remove inconsistent training examples
and retrain (default 0)
Performance estimation options:
-x [0,1] -> compute leave-one-out estimates (default 0)
(see [5])
-o ]0..2] -> value of rho for XiAlpha-estimator and for pruning
leave-one-out computation (default 1.0) (see [2])
-k [0..100] -> search depth for extended XiAlpha-estimator
(default 0)
Transduction options (see [3]):
-p [0..1] -> fraction of unlabeled examples to be classified
into the positive __class (default is the ratio of
positive and negative examples in the training data)
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
-d int -> parameter d in polynomial kernel
-g float -> parameter gamma in rbf kernel
-s float -> parameter s in sigmoid/poly kernel
-r float -> parameter c in sigmoid/poly kernel
-u string -> parameter of user defined kernel
Optimization options (see [1]):
-q [2..] -> maximum size of QP-subproblems (default 10)
-n [2..q] -> number of new variables entering the working set
in each iteration (default n = q). Set n<q to prevent
zig-zagging.
-m [5..] -> size of cache for kernel evaluations in MB (default 40)
The larger the faster...
-e float -> eps: Allow that error for termination criterion
[y [w*x+b] - 1] >= eps (default 0.001)
-y [0,1] -> restart the optimization from alpha values in file
specified by -a option. (default 0)
-h [5..] -> number of iterations a variable needs to be
optimal before considered for shrinking (default 100)
-f [0,1] -> do final optimality check for variables removed
by shrinking. Although this test is usually
positive, there is no guarantee that the optimum
was found if the test is omitted.(default 1)
-y string -> if option is given, reads alphas from file with given
and uses them as starting point. (default 'disabled')
-# int -> terminate optimization, if no progress after this
number of iterations. (default 100000)
Output options:
-l string -> file to write predicted labels of unlabeled
examples into after transductive learning
-a string -> write all alphas to this file after learning
(in the same order as in the training set)
More details in:
[1] T. Joachims, Making Large-Scale SVM Learning Practical. Advances in
Kernel Methods - Support Vector Learning, B. Schelkopf and C. Burges and
A. Smola (ed.), MIT Press, 1999.
[2] T. Joachims, Estimating the Generalization performance of an SVM
Efficiently. International Conference on Machine Learning (ICML), 2000.
[3] T. Joachims, Transductive Inference for Text Classification using Support
Vector Machines. International Conference on Machine Learning (ICML),
1999.
[4] K. Morik, P. Brockhausen, and T. Joachims, Combining statistical learning
with a knowledge-based approach - A case study in intensive care
monitoring. International Conference on Machine Learning (ICML), 1999.
[5] T. Joachims, Learning to Classify Text Using Support Vector
Machines: Methods, Theory, and Algorithms. Dissertation, Kluwer,
2002.
你问我生命中还有什么可追寻？