I am getting better accuracy with Euclidean distance for Zernike features than SVM or ANFIS. Is it possible?
2 次查看(过去 30 天)
显示 更早的评论
I have extracted Zernike Moment features from ORL face database and classified using SVM (libsvm tool), ANFIS of MATLAB, Euclidean distance and Backpropagation Algorithm. I am getting 93.5%, 90.5%, 96% and 90% accuracy respectively for each classifier. Highest accuracy has been obtained using simplest Euclidean distance measure. Is it really possible or I may be doing something wrong? Because as I see literature than SVM is definitely a better classifier. But in my case this is not happening. Please any suggestions/help?
0 个评论
回答(1 个)
Walter Roberson
2015-12-13
Zernike Moment features are rotational invariant (at least if you take their magnitude). Euclidean distance is rotational invariant. SVM involves finding a hyperplane in some space to discriminate between features, so SVM is directional in that hyperplane; whether it is rotational invariant in the original space depends on the mapping between the original space and the space the hyperplane is in... but chances are that it is not rotational invariant.
另请参阅
类别
在 Help Center 和 File Exchange 中查找有关 Fuzzy Logic Toolbox 的更多信息
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!