From 71b3605d5ccb89adf0fc9f517b3fa8c7e5522cbe Mon Sep 17 00:00:00 2001 From: Davis King Date: Sun, 7 Jul 2013 14:59:42 -0400 Subject: [PATCH] clarified spec --- dlib/svm/structural_svm_problem_abstract.h | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/dlib/svm/structural_svm_problem_abstract.h b/dlib/svm/structural_svm_problem_abstract.h index ed1a6cb10..672b4339e 100644 --- a/dlib/svm/structural_svm_problem_abstract.h +++ b/dlib/svm/structural_svm_problem_abstract.h @@ -34,14 +34,15 @@ namespace dlib - This object will not be verbose WHAT THIS OBJECT REPRESENTS - This object is a tool for solving the optimization problem associated - with a structural support vector machine. A structural SVM is a supervised + This object is a tool for solving the optimization problem associated with + a structural support vector machine. A structural SVM is a supervised machine learning method for learning to predict complex outputs. This is - contrasted with a binary classifier which makes only simple yes/no predictions. - A structural SVM, on the other hand, can learn to predict outputs as complex - as entire parse trees. To do this, it learns a function F(x,y) which measures - how well a particular data sample x matches a label y. When used for prediction, - the best label for a new x is given by the y which maximizes F(x,y). + contrasted with a binary classifier which makes only simple yes/no + predictions. A structural SVM, on the other hand, can learn to predict + complex outputs such as entire parse trees or DNA sequence alignments. To + do this, it learns a function F(x,y) which measures how well a particular + data sample x matches a label y. When used for prediction, the best label + for a new x is given by the y which maximizes F(x,y). To use this object you inherit from it, provide implementations of its four pure virtual functions, and then pass your object to the oca optimizer.