
Be the first to like this
Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.
Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.
Published on
I study classification problems in a standard learning framework, in which an unsupervised learner creates a discriminant function over each class and observations are labeled by the learner returning the highest value associated with that observation. I examine whether this approach gains significant advantage over traditional discriminant techniques.
Probably Approximately Correct learning distributions over class labels under L1 distance or KLdivergence is shown to imply PAC classification in this framework. I determine bounds on the regret associated with the resulting classifier, taking into account the possibility of variable misclassification penalties, and demonstrate the advantage of estimating the a posteriori probability distributions over class labels in the setting of Optical Character Recognition.
Unsupervised learners can be used to learn a class of probabilistic
concepts (stochastic rules denoting the probability that an observation has a positive
label in a 2class setting). I demonstrate a situation where unsupervised learners
can be used even when it is hard to learn distributions over class labels – in this case
the discriminant functions do not estimate the class probability densities.
If that isn't exciting enough, I then use a standard statemerging technique to PAClearn a class of probabilistic automata. The results show that by learning the distribution over outputs under the weaker L1 distance rather than KLdivergence we are able to learn without knowledge of the expected length of an output. It is also shown that for a restricted class of these automata learning under L1 distance is equivalent to learning under KLdivergence.
Clipping is a handy way to collect and organize the most important slides from a presentation. You can keep your great finds in clipboards organized around topics.
Be the first to comment