cjlin1
Training and Accuracy issue
I am computer science student from India. I am used to play with SVM implementation of liblinear from sklearn library in python. but recently I started converting my code from python to C++ and used LIBSVMs C_SVC it works perfectly for me giving me above 97% of accuracy.
But my data set is very large and training time is very slow on LIBSVM so I moved on LIBLINEAR to obtain multi core performance for training. and it is creating more furious problem for me that I am getting accuracy only around 15%.
DATASET:
2,50,000 Images of 7 different classes dimension 128 X 128 px calculate HOG features of all images, length of 1 feature vector is 1296 X* = 250000 x 1296 Y = 250000 whole data set is normalised in 0-1 range.
I am not using command line interface of LIBLINEAR because training file is getting very big in GBs. I am including liblinear and performed all necessary steps in order to use all the classes and functions of it.
now I have to classify all images into 7 different classes
I am using param.s=2 param.e=0.0001 don't need to set weight of different classes and perform cross fold validation 70 for 2,50,000 images to find value of C it gives me value of C about 4.76837e-07 and CV accuracy = 16.3265%
what should I do?? If I made any mistake please direct me on the correct path. thank you.
I suggest you consider a smaller subset and see if significant differences are observed by using libsvm/liblinear
itgandhi writes:
I am computer science student from India. I am used to play with SVM implementation of liblinear from sklearn library in python. but recently I started converting my code from python to C++ and used LIBSVMs C_SVC it works perfectly for me giving me above 97% of accuracy.
But my data set is very large and training time is very slow on LIBSVM so I moved on LIBLINEAR to obtain multi core performance for training. and it is creating more furious problem for me that I am getting accuracy only around 15%.
DATASET:
2,50,000 Images of 7 different classes dimension 128 X 128 px calculate HOG features of all images, length of 1 feature vector is 1296 X* = 250000 x 1296 Y = 250000 whole data set is normalised in 0-1 range.
I am not using command line interface of LIBLINEAR because training file is getting very big in GBs. I am including liblinear and performed all necessary steps in order to use all the classes and functions of it.
now I have to classify all images into 7 different classes
I am using param.s=2 param.e=0.0001 don't need to set weight of different classes and perform cross fold validation 70 for 2,50,000 images to find value of C it gives me value of C about 4.76837e-07 and CV accuracy = 16.3265%
what should I do?? If I made any mistake please direct me on the correct path. thank you.
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