Matches in DBpedia 2014 for { <http://dbpedia.org/resource/Hinge_loss> ?p ?o. }
Showing items 1 to 13 of
13
with 100 items per page.
- Hinge_loss abstract "In machine learning, the hinge loss is a loss function used for training classifiers. The hinge loss is used for "maximum-margin" classification, most notably for support vector machines (SVMs).For an intended output t = ±1 and a classifier score y, the hinge loss of the prediction y is defined asNote that y should be the "raw" output of the SVM's decision function, not the predicted class label. E.g., in linear SVMs, .It can be seen that when and have the same sign (meaning predicts the right class) and , , but when they have opposite sign, increases linearly with (one-sided error).".
- Hinge_loss wikiPageID "33100241".
- Hinge_loss wikiPageRevisionID "585950454".
- Hinge_loss hasPhotoCollection Hinge_loss.
- Hinge_loss subject Category:Loss_functions.
- Hinge_loss subject Category:Support_vector_machines.
- Hinge_loss comment "In machine learning, the hinge loss is a loss function used for training classifiers. The hinge loss is used for "maximum-margin" classification, most notably for support vector machines (SVMs).For an intended output t = ±1 and a classifier score y, the hinge loss of the prediction y is defined asNote that y should be the "raw" output of the SVM's decision function, not the predicted class label.".
- Hinge_loss label "Hinge loss".
- Hinge_loss sameAs m.0h64g00.
- Hinge_loss sameAs Q5767098.
- Hinge_loss sameAs Q5767098.
- Hinge_loss wasDerivedFrom Hinge_loss?oldid=585950454.
- Hinge_loss isPrimaryTopicOf Hinge_loss.