Training and Accuracy issue

[simsong]

From @itgandhi on November 3, 2017 11:16

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.

Copied from original issue: cjlin1#39

[simsong]

From @cjlin1 on November 3, 2017 12:30

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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