Late Fusion with SVM and Neural Network

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I have a question regarding the process to make a late fusion between SVM (Linear) and a NeuralNetwork (NN),

I have done some research and I found that concatenated the clf.predict_prob of SVM and Model.predic of NN, I should train the new model, however, these scores are for the test data and I cannot figure what to do with the training data.

In other words, I train the new model with the concatenated probability scores of the test data from my two models (SVM and NN) and I test this new model with the same concatenated data, and I'm not really sure of this.

Can you please give me an insight into if this is correct?

tensorflow svm python
1 Answers

After a lot of searching and research I found the solution:

The solution is to train and test a new classifier, in my case it was another Neural Network, with the concatenated probability scores obtained from both data sets (training and test), of the two classifiers, the Linear SVM and the Neural Network.

An example of this of three Linear SVM Late fusion was implemented in python, and can be found in the following link:

https://github.com/JMalhotra7/Learning-image-by-parts-using-early-and-late-fusion-of-auto-encoder-features

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