In structured prediction problems, outputs are not confined to binary labels; they are often complex objects such as sequences, trees, or alignments. Support Vector Machine (SVM) ...
In this paper we eliminate the need for parameter estimation associated with the set covering machine (SCM) by directly minimizing generalization error bounds. Firstly, we consider...
We present a distributed machine learning framework based on support vector machines that allows classification problems to be solved iteratively through parallel update algorithm...
In recent years, there has been a cross-fertilization of ideas between computational neuroscience models of the operation of the neocortex and artificial intelligence models of mac...
John Thornton, Jolon Faichney, Michael Blumenstein...
We discuss a simple sparse linear problem that is hard to learn with any algorithm that uses a linear combination of the training instances as its weight vector. The hardness holds...