LBR has demonstrated outstanding classification accuracy. However, it has high computational overheads when large numbers of instances are classified from a single training set. We compare LBR and the tree-augmented Bayesian classifier, and present a new heuristic LBR classifier that combines elements of the two. It requires less computation than LBR, but demonstrates similar prediction accuracy.

We present an actor-critic scheme for reinforcement learning in complex domains. The main contribution is to show that planning and I/O dynamics can be separated such that an intractable planning problem reduces to a simple multi-armed bandit problem, where each lever stands for a potentially arbitrarily complex policy. Furthermore, we use the Bayesian control rule to construct an adaptive band...

Bayesian model averaging (BMA) can be seen as the optimal approach to any induction task. It can reduce error by accounting for model uncertainty in a principled way, and its usefulness in several areas has been empirically veri ed. However, few attempts to apply it to rule induction have been made. This paper reports a series of experiments designed to test the utility of BMA in this eld. BMA ...

Several counterparts of Bayesian networks based on different paradigms have been proposed in evidence theory. Nevertheless, none of them is completely satisfactory. In this paper we will present a new one, based on a recently introduced concept of conditional independence. We define a conditioning rule for variables, and the relationship between conditional independence and irrelevance is studi...

Existing probabilistic approaches to automated reasoning impose severe restrictions on its knowledge representation scheme. Mainly, this is to ensure that there exists an eeective inferencing algorithm. Unfortunately , this makes the application of these approaches to general domains quite diicult. In this paper, we present a new model called Bayesian multi-networks which uses a rule-based orga...

If the selection of an adequate prior was the major conceptual and modeling challenge of Bayesian analysis, the major implementational challenge is computation. As soon as the model deviates from the conjugate structure, finding the posterior (first the marginal) distribution and the Bayes rule is all but simple. A closed form solution is more an exception than the rule, and even for such close...

This paper introduces two novel approaches for extracting compact grammars for hierarchical phrase-based translation. The first is a combinatorial optimization approach and the second is a Bayesian model over Hiero grammars using Variational Bayes for inference. In contrast to the conventional Hiero (Chiang, 2007) rule extraction algorithm , our methods extract compact models reducing model siz...

The application of expected utility theory to construct adaptive agents is both computationally intractable and statistically questionable. To overcome these difficulties, agents need the ability to delay the choice of the optimal policy to a later stage when they have learned more about the environment. How should agents do this optimally? An information-theoretic answer to this question is gi...

This chapter discusses a multimodal biometric sensor fusion approach for controlling building access. A Bayesian framework is implemented fusing the decisions received from multiple biometric sensors and achieving the desired system accuracy. The optimal rule is a function of the error cost and a priori probability of an intruder. The chapter presents and then analyzes a Bayesian framework for ...