Conventional classification learning allows a classifier to make a one shot decision in order to identify the correct label. However, in many practical applications, the problem ...
We consider the problem of learning a ranking function, that is a mapping from instances to rankings over a finite number of labels. Our learning method, referred to as ranking by...
—The error floor of bit-interleaved coded modulation with iterative decoding (BICM-ID) can be minimized for a particular constellation by maximizing the harmonic mean of the squ...
Matthew C. Valenti, Raghu Doppalapudi, Don J. Torr...
We consider the problem of reducing the dimensionality of labeled data for classification. Unfortunately, the optimal approach of finding the low-dimensional projection with minim...
We study distribution-dependent, data-dependent, learning in the limit with adversarial disturbance. We consider an optimization-based approach to learning binary classifiers from...