Training and Accuracy issue

[itgandhi]

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.

[cjlin1]

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. — You are receiving this because you are subscribed to this thread. Reply to this email directly, view it on GitHub, or mute the thread.*

[simsong]

This issue was moved to angleto#5