Supervised learning deals with the inference of a distribution over an output or label space $\CY$ conditioned on points in an observation space $\CX$, given a training dataset $D$...
This paper presents a comparative study of five parameter estimation algorithms on four NLP tasks. Three of the five algorithms are well-known in the computational linguistics com...
Jianfeng Gao, Galen Andrew, Mark Johnson, Kristina...
Learning undirected graphical models such as Markov random fields is an important machine learning task with applications in many domains. Since it is usually intractable to learn...
Arthur Asuncion, Qiang Liu, Alexander T. Ihler, Pa...
Studying the learnability of classes of recursive functions has attracted considerable interest for at least four decades. Starting with Gold's (1967) model of learning in th...
We propose a method for choosing the number of colors or true gray levels in an image; this allows fully automatic segmentation of images. Our underlying probability model is a hid...