A friend said that epitome is pronounced as epi-tuh-mi and not epi-tome (with the tome like home). 
Who is right? Also, is the pronunciation purely dependent on the region where you learnt English?


Question: Can I use an SVM for labeling data as more than one class
Answer: SVM are not designed to estimate probability of a class. Nevertheless, there is a way to estimate such probabilities, using Platt scaling.

There is SVM-like probabilistic method, namely, Relevance Vector Machines. Unfortunately, they're patented by Microsoft, though, apparently, free to use in academia.

Finally, if you want to find these xs where SVM is unsure about correct labeling, you can do it even without probability. The decision rule of SVM is

$$
class(x) = \begin{cases}
1, &amp; \mathbf w^T x + b &gt; 0 \\
0, &amp; \mathbf w^T x + b &lt; 0 
\end{cases}
$$

Where $\mathbf w^T x + b$ is SVM's decision function, effectively, a signed distance to the separating hyperplane. If this distance (modulo of it) is too small — SVM is less sure about $x$. You can come up with some kind of threshold on decision functions to find vectors of interest.



About MaxEnt vs. (linear) SVM:

It's mostly opinion-based, but their loss functions are quite similar



SVM's loss is called "Hinge loss", MaxEnt's — "Logistic".

Asymptotically (as signed distance goes to $\pm \infty$) they're the same (modulo scaling constant because of arbitrary log base in Logistic loss), so if you data is well-separable, both should perform fine. Both methods will find a separating hyperplane, though, SVM's one will have a bigger margin (because it's a max-margin classifier).

When data is not linear separable, SVM will try to push as many points behind the boundary of distance = $\pm$ 1, thus having 0 loss on them, while logistic loss may tolerate some errors to become even more confident in already correctly labeled samples.

This is my opinion, it is not necessarily true :-)