classifiers

The fact that all classifiers -- including human beings -- fail in some cases is a separate issue.

You can also play with different classifiers to see how they behave (training time and classification performance).

It is all self-reporting based on the classifiers given to PyPI (NetworkX lists itself as supporting Python 3, Python3.2–Python3.4).

This analogy doesn't hold, because the whole point of these classifiers is to classify things the same way humans would.

No theory (random classifiers) aggregated to optimize on a non representative hold out set form a theory on that set?

If you create classifiers that express some domain theory on the training set in step 1.

Pretty much ALL machine learning image classifiers suffer from these pathologies from Szegedy et al.

Also very interested to see what kind of Machine Learning / classifiers they are using.

I was using scikit-learn and just swapping classifiers in and out trying different ones as well as trying different parameters.

It seems like you could use it in a whole bunch of places to bypass regex/classifiers, or potentially a lot of things without proper encoding.

There needs to be more promotion of this, as this is the first time I've heard about the version specific classifiers.

Upcoming projects include refactoring key components into services, building out automated call scoring using machine classifiers, deeper integrations with other services, and improving our analytics tools.

If one paired the present classifiers with Amazon Mechanical Turk, just providing one bit of information -- "is-it-an-animal?" -- I wonder how well the current classifiers would fare in relation to human beings?

XPS uses a variety of AI technologies such as IBM's Watson, Named Entity Recognition, Natural Language Classifiers, and Sentiment Analysis to produce its results.