svm_struct_api_types.h

svm(支持向量机)分类算法本质上是二类分类器

/***********************************************************************/
/*                                                                     */
/*   svm_struct_api.h                                                  */
/*                                                                     */
/*   Definition of API for attaching implementing SVM learning of      */
/*   structures (e.g. parsing, multi-label classification, HMM)        */ 
/*                                                                     */
/*   Author: Thorsten Joachims                                         */
/*   Date: 13.10.03                                                    */
/*                                                                     */
/*   Copyright (c) 2003  Thorsten Joachims - All rights reserved       */
/*                                                                     */
/*   This software is available for non-commercial use only. It must   */
/*   not be modified and distributed without prior permission of the   */
/*   author. The author is not responsible for implications from the   */
/*   use of this software.                                             */
/*                                                                     */
/***********************************************************************/

#ifndef svm_struct_api_types
#define svm_struct_api_types

# define INST_NAME          "Multi-Class SVM"
# define INST_VERSION       "V1.01"
# define INST_VERSION_DATE  "01.09.04"

typedef struct pattern {
  /* this defines the x-part of a training example, e.g. the structure
     for storing a natural language sentence in NLP parsing */
  DOC *doc;
} PATTERN;

typedef struct label {
  /* this defines the y-part (the label) of a training example,
     e.g. the parse tree of the corresponding sentence. */
  int class;
} LABEL;

typedef struct structmodel {
  double *w;          /* pointer to the learned weights */
  MODEL  *svm_model;  /* the learned SVM model */
  long   sizePsi;     /* maximum number of weights in w */
  /* other information that is needed for the stuctural model can be
     added here, e.g. the grammar rules for NLP parsing */
} STRUCTMODEL;

typedef struct struct_learn_parm {
  double epsilon;              /* precision for which to solve
				  quadratic program */
  double newconstretrain;      /* number of new constraints to
				  accumulate before recomputing the QP
				  solution */
  double C;                    /* trade-off between margin and loss */
  char   custom_argv[20][300]; /* string set with the -u command line option */
  int    custom_argc;          /* number of -u command line options */
  int    slack_norm;           /* norm to use in objective function
                                  for slack variables; 1 -> L1-norm, 
				  2 -> L2-norm */
  int    loss_type;            /* selected loss function from -r
				  command line option. Select between
				  slack rescaling (1) and margin
				  rescaling (2) */
  int    loss_function;        /* select between different loss
				  functions via -l command line
				  option */
  /* further parameters that are passed to init_struct_model() */
  int num_classes;
  int num_features;
} STRUCT_LEARN_PARM;

typedef struct struct_test_stats {
  /* you can add variables for keeping statistics when evaluating the
     test predictions in svm_struct_classify. This can be used in the
     function eval_prediction and print_struct_testing_stats. */
} STRUCT_TEST_STATS;

#endif