Active improvement of hierarchical object features under budget constraints

Refinement of Observations under Budget Constraints in Active Learning

This paper addresses a new challenge in active learning: objects are described in different levels of quality and we can obtain a better version of an object at a given cost. This setting is very useful in robotics, where computing and time resources are limited. We aim to improve and label only a few objects under a given budget in an active learning setting.

Supervised Sequential Classification Under Budget Constraints

In this paper we develop a framework for a sequential decision making under budget constraints for multi-class classification. In many classification systems, such as medical diagnosis and homeland security, sequential decisions are often warranted. For each instance, a sensor is first chosen for acquiring measurements and then based on the available information one decides (rejects) to seek mo...

Crowdsourcing Complex Workflows under Budget Constraints

We consider the problem of task allocation in crowdsourcing systems with multiple complex workflows, each of which consists of a set of inter-dependent micro-tasks. We propose Budgeteer, an algorithm to solve this problem under a budget constraint. In particular, our algorithm first calculates an efficient way to allocate budget to each workflow. It then determines the number of inter-dependent...

Selecting a Response Plan Under Budget Constraints

Very Fast Kernel SVM under Budget Constraints

In this paper we propose a fast online Kernel SVM algorithm under tight budget constraints. We propose to split the input space using LVQ and train a Kernel SVM in each cluster. To allow for online training, we propose to limit the size of the support vector set of each cluster using different strategies. We show in the experiment that our algorithm is able to achieve high accuracy while having...