<p>This is completely possible and actually quite common. You just select the output of a layer of the neural network and use that as a feature vector to train a SVM. Generally one normalizes the feature vectors as well.</p>

<p>Features learned by (Convolutional) Neural Networks are powerful enough that they generalize to different kinds of objects and even completely different images. For examples see the paper <a href="https://arxiv.org/abs/1403.6382" rel="noreferrer">CNN Features off-the-shelf: an Astounding Baseline for Recognition</a>.</p>

<p>About implementation, you just have to train a neural network, then select one of the layers (usually the ones right before the fully connected layers or the first fully connected one), run the neural network on your dataset, store all the feature vectors, then train an SVM with a different library (e.g sklearn).</p>
Keras Neural Networks and SKlearn SVM.SVC
<p>Lately I was on a Data Science meetup in my city, there was a talk about connecting Neural Networks with SVM. Unfortunately presenter had to quit right after presentation, so I wasn't able to ask some questions.</p>

<p>I was wondering how is that possible ? He was talking about using neural networks for his classification, and later on, he was using SVM classifier to improve his accuracy and precision by about 10%. </p>

<p>I am using Keras for Neural Networks and SKlearn for the rest of ML. </p>