We use Bayesian methods to design cellular neural networks for signal processing tasks and the Boltzmann Machine learning rule for parameter estimation. The learning rule can be used for models with uhidden" units, or for compietely unsupervised learning. The latter is exemplified by unsupervised adaptation of an image segmentation cellular network, in particular we apply the learning rule to a...

The authors present and test a new method of teaching Bayesian reasoning, something about which previous teaching studies reported little success. Based on G. Gigerenzer and U. Hoffrage's (1995) ecological framework, the authors wrote a computerized tutorial program to train people to construct frequency representations (representation training) rather than to insert probabilities into Bayes's ...

Based on an earlier study on lazy Bayesian rule learning, this paper introduces a general lazy learning framework, called LazyRule, that begins to learn a rule only when classifying a test case. The objective of the framework is to improve the performance of a base learning algorithm. It has the potential to be used for diierent types of base learning algorithms. LazyRule performs attribute eli...

Based on an earlier study on lazy Bayesian rule learning, this paper introduces a general lazy learning framework, called LazyRule, that begins to learn a rule only when classifying a test case. The objective of the framework is to improve the performance of a base learning algorithm. It has the potential to be used for diierent types of base learning algorithms. LazyRule performs attribute eli...

Optional stopping refers to the practice of peeking at data and then, based on the results, deciding whether or not to continue an experiment. In the context of ordinary significance-testing analysis, optional stopping is discouraged, because it necessarily leads to increased type I error rates over nominal values. This article addresses whether optional stopping is problematic for Bayesian inf...

MOTIVATION Disease state prediction from biomarker profiling studies is an important problem because more accurate classification models will potentially lead to the discovery of better, more discriminative markers. Data mining methods are routinely applied to such analyses of biomedical datasets generated from high-throughput 'omic' technologies applied to clinical samples from tissues or bodi...

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...