A key challenge for dialogue-based intelligent tutoring systems lies in selecting follow-up questions that are not only context relevant but also encourage self-expression and stimulate learning. This paper presents an approach to ranking candidate questions for a given dialogue context and introduces an evaluation framework for this task. We learn to rank using judgments collected from expert ...

We consider (ǫ, δ)-PAC maximum-selection and ranking for general probabilistic models whose comparisons probabilities satisfy strong stochastic transitivity and stochastic triangle inequality. Modifying the popular knockout tournament, we propose a maximum-selection algorithm that uses O (

Ranking is a central problem in information retrieval. Much work has been done in the recent years to automate the development of ranking models by means of supervised machine learning. Feature selection aims to provide sparse models which are computationally efficient to evaluate, and have good ranking performance. We propose integrating the feature selection as part of the training process fo...

Ranking is a central problem in information retrieval. Much work has been done in the recent years to automate the development of ranking models by means of supervised machine learning. Feature selection aims to provide sparse models which are computationally efficient to evaluate, and have good ranking performance. We propose integrating the feature selection as part of the training process fo...

Under a Bayesian framework, we formulate the fully sequential sampling and selection decision in statistical ranking and selection as a stochastic control problem, and derive the associated Bellman equation. Using value function approximation, we derive an approximately optimal allocation policy. We show that this policy is not only computationally efficient but also possesses both one-step-ahe...

Whereas the variable selection has been extensively studied in the context of supervised learning, the unsupervised variable selection has attracted attention of researchers more recently as the available amount of unlabeled data has exploded. Many unsupervised variable ranking criteria were proposed and their relevance is usually demonstrated using either external cluster validity indexes or t...

The problem of feature selection for Support Vector Machines (SVMs) classification is investigated in the linear two classes case. We suggest a new method of feature selection based on ranking scores derived from SVMs. We analyze the retraining effects on the ranking rules based on these scores. Our features selection algorithm consists in a forward selection strategy according to the decreasin...