Sparse kernel SVMs via cutting-plane training
We explore an algorithm for training SVMs with Kernels that can represent the learned rule using arbitrary basis vectors, not just the support vectors (SVs) from the training set. This results in two benefits. First, the added flexibility makes it ...
Learning multi-linear representations of distributions for efficient inference
We examine the class of multi-linear representations (MLR) for expressing probability distributions over discrete variables. Recently, MLR have been considered as intermediate representations that facilitate inference in distributions represented as ...
Combining instance-based learning and logistic regression for multilabel classification
Multilabel classification is an extension of conventional classification in which a single instance can be associated with multiple labels. Recent research has shown that, just like for conventional classification, instance-based learning algorithms ...
On structured output training: hard cases and an efficient alternative
We consider a class of structured prediction problems for which the assumptions made by state-of-the-art algorithms fail. To deal with exponentially sized output sets, these algorithms assume, for instance, that the best output for a given input can be ...
Hybrid least-squares algorithms for approximate policy evaluation
The goal of approximate policy evaluation is to "best" represent a target value function according to a specific criterion. Different algorithms offer different choices of the optimization criterion. Two popular least-squares algorithms for performing ...
A self-training approach to cost sensitive uncertainty sampling
Uncertainty sampling is an effective method for performing active learning that is computationally efficient compared to other active learning methods such as loss-reduction methods. However, unlike loss-reduction methods, uncertainty sampling cannot ...
Cost-sensitive learning based on Bregman divergences
This paper analyzes the application of a particular class of Bregman divergences to design cost-sensitive classifiers for multiclass problems. We show that these divergence measures can be used to estimate posterior probabilities with maximal accuracy ...