Abstract
Sensor networks act in dynamic environments with distributed sources of continuous data and computing with resource constraints. Learning in these environments is faced with new challenges: the need to continuously maintain a decision model consistent with the most recent data. Desirable properties of learning algorithms include: the ability to maintain an any time model; the ability to modify the decision model whenever new information is available; the ability to forget outdated information; and the ability to detect and react to changes in the underlying process generating data, monitoring the learning process and managing the trade-off between the cost of updating a model and the benefits in performance gains. In this chapter we illustrate these ideas in two learning scenarios - centralized and distributed - and present illustrative algorithms for these contexts. © 2007 Springer-Verlag Berlin Heidelberg.

Abstract

Abstract
We present Adaptive Bayes, an adaptive incremental version of Naive Bayes, to model a prediction task based on learning styles in the context of an Adaptive Hypermedia Educational System. Since the student's preferences can change over time, this task is related to a problem known as concept drift in the machine learning community. For this class of problems an adaptive predictive model, able to adapt quickly to the user's changes, is desirable. The results from conducted experiments show that Adaptive Bayes seems to be a fine and simple choice for this kind of prediction task in user modeling.

Abstract
Naive Bayes is a well known and studied algorithm both in statistics and machine learning. Although its limitations with respect to expressive power, this procedure has a surprisingly good performance in a wide variety of domains, including many where there are clear dependencies between attributes. In this paper we address its main perceived limitation - its inability to deal with attribute dependencies. We present Linear Bayes that uses, for the continuous attributes, a multivariate normal distribution to compute the require probabilities. In this way, the interdependencies between the continuous attributes are considered. On the empirical evaluation, we compare Linear Bayes against a naive-Bayes that discretize continuous attributes, a naive-Bayes that assumes a univariate Gaussian for continuous attributes, and a standard Linear discriminant function. We show that Linear Bayes is a plausible algorithm, that competes quite well against other well established techniques.

Abstract
Several researchers have studied the application of Machine Learning techniques to the task of user modeling. As most of them pointed out, this task requires learning algorithms that should work on-line, incorporate new information incrementality, and should exhibit the capacity to deal with concept-drift. In this paper we present Adaptive Bayes, an extension to the well-known naive-Bayes, one of the most common used learning algorithms for the task of user modeling. Adaptive Bayes is an incremental learning algorithm that could work on-line. We have evaluated Adaptive Bayes on both frameworks. Using a set of benchmark problems from the UCI repository [2], and using several evaluation statistics, all the adaptive systems show significant advantages in comparison against their non-adaptive versions.

Abstract
The purpose of this paper is to describe an adaptive algorithm for improving the performance of Bayesian Network Classifiers (BNCs) in an on-line learning framework. Instead of choosing a priori a particular model class of BNCs, our adaptive algorithm scales up the model's complexity by gradually increasing the number of allowable dependencies among features, Starting with the simple Naive Bayes structure, it uses simple decision rules based on qualitative information about the performance's dynamics to decide when it makes sense to do the next move in the spectrum of feature dependencies and to start searching for a more complex classifier. Results in conducted experiments using the class of Dependence Bayesian Classifiers on three large datasets show that our algorithm is able to select a model with the appropriate complexity for the current amount of training data, thus balancing the computational cost of updating a model with the benefits of increasing in accuracy.