void print_help()
{
printf("\nSVM-struct learning module: %s, %s, %s\n",INST_NAME,INST_VERSION,INST_VERSION_DATE);
printf(" includes SVM-struct %s for learning complex outputs, %s\n",STRUCT_VERSION,STRUCT_VERSION_DATE);
printf(" includes SVM-light %s quadratic optimizer, %s\n",VERSION,VERSION_DATE);
copyright_notice();
printf(" usage: svm_struct_learn [options] example_file model_file\n\n");
printf("Arguments:\n");
printf(" example_file-> file with training data\n");
printf(" model_file -> file to store learned decision rule in\n");
printf("General Options:\n");
printf(" -? -> this help\n");
printf(" -v [0..3] -> verbosity level (default 1)\n");
printf(" -y [0..3] -> verbosity level for svm_light (default 0)\n");
printf("Learning Options:\n");
printf(" -c float -> C: trade-off between training error\n");
printf(" and margin (default 0.01)\n");
printf(" -p [1,2] -> L-norm to use for slack variables. Use 1 for L1-norm,\n");
printf(" use 2 for squared slacks. (default 1)\n");
printf(" -o [1,2] -> Rescaling method to use for loss.\n");
printf(" 1: slack rescaling\n");
printf(" 2: margin rescaling\n");
printf(" (default %d)\n",DEFAULT_RESCALING);
printf(" -l [0..] -> Loss function to use.\n");
printf(" 0: zero/one loss\n");
printf(" ?: see below in application specific options\n");
printf(" (default %d)\n",DEFAULT_LOSS_FCT);
printf("Optimization Options (see [2][5]):\n");
printf(" -w [0,..,9] -> choice of structural learning algorithm (default %d):\n",(int)DEFAULT_ALG_TYPE);
printf(" 0: n-slack algorithm described in [2]\n");
printf(" 1: n-slack algorithm with shrinking heuristic\n");
printf(" 2: 1-slack algorithm (primal) described in [5]\n");
printf(" 3: 1-slack algorithm (dual) described in [5]\n");
printf(" 4: 1-slack algorithm (dual) with constraint cache [5]\n");
printf(" 9: custom algorithm in svm_struct_learn_custom.c\n");
printf(" -e float -> epsilon: allow that tolerance for termination\n");
printf(" criterion (default %f)\n",DEFAULT_EPS);
printf(" -k [1..] -> number of new constraints to accumulate before\n");
printf(" recomputing the QP solution (default 100) (-w 0 and 1 only)\n");
printf(" -f [5..] -> number of constraints to cache for each example\n");
printf(" (default 5) (used with -w 4)\n");
printf(" -b [1..100] -> percentage of training set for which to refresh cache\n");
printf(" when no epsilon violated constraint can be constructed\n");
printf(" from current cache (default 100%%) (used with -w 4)\n");
printf("SVM-light Options for Solving QP Subproblems (see [3]):\n");
printf(" -n [2..q] -> number of new variables entering the working set\n");
printf(" in each svm-light iteration (default n = q). \n");
printf(" Set n < q to prevent zig-zagging.\n");
printf(" -m [5..] -> size of svm-light cache for kernel evaluations in MB\n");
printf(" (default 40) (used only for -w 1 with kernels)\n");
printf(" -h [5..] -> number of svm-light iterations a variable needs to be\n");
printf(" optimal before considered for shrinking (default 100)\n");
printf(" -# int -> terminate svm-light QP subproblem optimization, if no\n");
printf(" progress after this number of iterations.\n");
printf(" (default 100000)\n");
printf("Kernel Options:\n");
printf(" -t int -> type of kernel function:\n");
printf(" 0: linear (default)\n");
printf(" 1: polynomial (s a*b+c)^d\n");
printf(" 2: radial basis function exp(-gamma ||a-b||^2)\n");
printf(" 3: sigmoid tanh(s a*b + c)\n");
printf(" 4: user defined kernel from kernel.h\n");
printf(" -d int -> parameter d in polynomial kernel\n");
printf(" -g float -> parameter gamma in rbf kernel\n");
printf(" -s float -> parameter s in sigmoid/poly kernel\n");
printf(" -r float -> parameter c in sigmoid/poly kernel\n");
printf(" -u string -> parameter of user defined kernel\n");
printf("Output Options:\n");
printf(" -a string -> write all alphas to this file after learning\n");
printf(" (in the same order as in the training set)\n");
printf("Application-Specific Options:\n");
print_struct_help();
wait_any_key();
printf("\nMore details in:\n");
printf("[1] T. Joachims, Learning to Align Sequences: A Maximum Margin Aproach.\n");
printf(" Technical Report, September, 2003.\n");
printf("[2] I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun, Large Margin\n");
printf(" Methods for Structured and Interdependent Output Variables, Journal\n");
printf(" of Machine Learning Research (JMLR), Vol. 6(Sep):1453-1484, 2005.\n");
printf("[3] T. Joachims, Making Large-Scale SVM Learning Practical. Advances in\n");
printf(" Kernel Methods - Support Vector Learning, B. Schölkopf and C. Burges and\n");
printf(" A. Smola (ed.), MIT Press, 1999.\n");
printf("[4] T. Joachims, Learning to Classify Text Using Support Vector\n");
printf(" Machines: Methods, Theory, and Algorithms. Dissertation, Kluwer,\n");
printf(" 2002.\n");
printf("[5] T. Joachims, T. Finley, Chun-Nam Yu, Cutting-Plane Training of Structural\n");
printf(" SVMs, Machine Learning Journal, to appear.\n");
}
void print_help()
{
printf("\nSVM-struct learning module: %s, %s, %s\n",INST_NAME,INST_VERSION,INST_VERSION_DATE);
printf(" includes SVM-struct %s for learning complex outputs, %s\n",STRUCT_VERSION,STRUCT_VERSION_DATE);
printf(" includes SVM-light %s quadratic optimizer, %s\n",VERSION,VERSION_DATE);
copyright_notice();
printf(" usage: svm_struct_learn [options] example_file model_file\n\n");
printf("Arguments:\n");
printf(" example_file-> file with training data\n");
printf(" model_file -> file to store learned decision rule in\n");
printf("General options:\n");
printf(" -? -> this help\n");
printf(" -v [0..3] -> verbosity level (default 1)\n");
printf(" -y [0..3] -> verbosity level for svm_light (default 0)\n");
printf("Learning options:\n");
printf(" -c float -> C: trade-off between training error\n");
printf(" and margin (default 0.01)\n");
printf(" -p [1,2] -> L-norm to use for slack variables. Use 1 for L1-norm,\n");
printf(" use 2 for squared slacks. (default 1)\n");
printf(" -o [1,2] -> Slack rescaling method to use for loss.\n");
printf(" 1: slack rescaling\n");
printf(" 2: margin rescaling\n");
printf(" (default 1)\n");
printf(" -l [0..] -> Loss function to use.\n");
printf(" 0: zero/one loss\n");
printf(" (default 0)\n");
printf("Kernel options:\n");
printf(" -t int -> type of kernel function:\n");
printf(" 0: linear (default)\n");
printf(" 1: polynomial (s a*b+c)^d\n");
printf(" 2: radial basis function exp(-gamma ||a-b||^2)\n");
printf(" 3: sigmoid tanh(s a*b + c)\n");
printf(" 4: user defined kernel from kernel.h\n");
printf(" -d int -> parameter d in polynomial kernel\n");
printf(" -g float -> parameter gamma in rbf kernel\n");
printf(" -s float -> parameter s in sigmoid/poly kernel\n");
printf(" -r float -> parameter c in sigmoid/poly kernel\n");
printf(" -u string -> parameter of user defined kernel\n");
printf("Optimization options (see [2][3]):\n");
printf(" -q [2..] -> maximum size of QP-subproblems (default 10)\n");
printf(" -n [2..q] -> number of new variables entering the working set\n");
printf(" in each iteration (default n = q). Set n<q to prevent\n");
printf(" zig-zagging.\n");
printf(" -m [5..] -> size of cache for kernel evaluations in MB (default 40)\n");
printf(" The larger the faster...\n");
printf(" -e float -> eps: Allow that error for termination criterion\n");
printf(" (default 0.01)\n");
printf(" -h [5..] -> number of iterations a variable needs to be\n");
printf(" optimal before considered for shrinking (default 100)\n");
printf(" -k [1..] -> number of new constraints to accumulate before\n");
printf(" recomputing the QP solution (default 100)\n");
printf(" -# int -> terminate optimization, if no progress after this\n");
printf(" number of iterations. (default 100000)\n");
printf("Output options:\n");
printf(" -a string -> write all alphas to this file after learning\n");
printf(" (in the same order as in the training set)\n");
printf("Structure learning options:\n");
print_struct_help();
wait_any_key();
printf("\nMore details in:\n");
printf("[1] T. Joachims, Learning to Align Sequences: A Maximum Margin Aproach.\n");
printf(" Technical Report, September, 2003.\n");
printf("[2] I. Tsochantaridis, T. Hofmann, T. Joachims, and Y. Altun, Support Vector \n");
printf(" Learning for Interdependent and Structured Output Spaces, ICML, 2004.\n");
printf("[3] T. Joachims, Making Large-Scale SVM Learning Practical. Advances in\n");
printf(" Kernel Methods - Support Vector Learning, B. Schölkopf and C. Burges and\n");
printf(" A. Smola (ed.), MIT Press, 1999.\n");
printf("[4] T. Joachims, Learning to Classify Text Using Support Vector\n");
printf(" Machines: Methods, Theory, and Algorithms. Dissertation, Kluwer,\n");
printf(" 2002.\n\n");
}
