I'm considering using Weka for my data processing. Therefore, I'm testing Weka's algorithms on well-known datasets such as Kaggle's Titanic dataset. However, I noticed some accuracy differences when using the same classifier (Random Forest) for my dataset. I want to know what are the equivalent hyperparameters between Weka and Python's Scikit learn implementation of the random forest algorithm so I can use the algorithm appropriately in Weka just as I used to do it in Python.
I'm trying to use caffe to simulate the SGDclassifier and Logisticregression linear models in sklearn. As we all know, in caffe, one "InnerProduct" layer plus one "Softmaxwithloss" layer represent a logistic regression Y = Logit(WX+b).
I'm now using the digits dataset in the sklearn datasets package as the trianing set(5/6 of all the data-label pairs) and testing set(the rest 1/6). However, the accuracy obtained by SGDclassifer() or LogisticRegression() could reach nearly 90%, while the accuracy obtained by two-layer Neural Network cannot exceed 30% after training. Is this because of the parameter settings or something else? The gap between them is just kind of too large.
I have implemented character recognition using a library
but I still don't get how SVM theory works in training and prediction process, I just understand SVM is only finding the hyperplane
E.g., suppose I have a training image as follows
image from google, number zero
How do we find hyperplane for each training data like above?
How is the prediction process is done?
How can the SVM classify the data based on those hyperplane?
Thank you very much if you can help me
You can use opencv and python.Opencv has implemented svm and you can use it by function call.
SVM is machine leraning model for data classification.We can use SVM to classify images.the steps are
you must have a training dataset(a dataset of images whose labels are known)
Extract features [features are color,shape,hog,surf,sift etc..] from that images and store that,also store the assosiated labels
then train svm using these datas
Now you can use svm to predict labels of unkonwn images
this link will help you
First, It is a non linear separable problem you have to implement kernel SVM which projects them into higher dimensional space where it becomes linearly separable. You can use sklearn library to achieve the above.
I would like to know how to calculate the probability using Spark MLLIB SVM in a multi-class classification problem.
The documentation shows there is no such function available.
LibSVM uses Platt-scaling.
My questions are:
Is there a function to calculate the probability somewhere?
If not, who can help me implementing such functionality?
I would simply take the average distances from all the support vectors for each category after training and compare the distance from a new data-point to hyperplanes from all the classifiers.
I think the SVMModel.predict() gives these distances, but I am uncertain.
I have two classes and several variables. After training the SVM, it gives me a good accuracy on prediction of testing data classes. Does anybody know how can I find out which of my variables are less important in the prediction done by SVM ? I'm nearly new in SVM and I'm just familiar with console interface and matlab interface of SVM. Is there any option to achive the importance of variables for SVM after training or prediction phase ?