Leave one out cross validation-HELP
1 Ansicht (letzte 30 Tage)
Ältere Kommentare anzeigen
Hi,
I am implementing a fingerprint liveness detection algorithm by extracting the features using a modified combination of Weber local descriptor and Centralized Binary pattern, a 9x32 vector matrix is generated. So after extracting the features from the training dataset of 2011 Liveness Detection Competition the resulting vector is 18000x32. So I want to use the Leave one out cross validation to determine its accuracy. Could you please give an example? So far I have seen examples of KFold cross validation in the documentation but none for leaveout. At the moment I used a (10)KFold cross validation to train the resulting vector then extracted the code and did some minor changes such as: partitionedModel = crossval(trainedClassifier.ClassificationEnsemble, 'Kfold', 10)=>partitionedModel = crossval(trainedClassifier.ClassificationEnsemble, 'Leaveout', 'on'); Could you please point me to the right direction?
Thank you very much
0 Kommentare
Antworten (0)
Siehe auch
Kategorien
Mehr zu Get Started with Deep Learning Toolbox finden Sie in Help Center und File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!