The generalization performance of two learning algorithms, Bayes algorithm and the ‘‘optimal learning’’ algorithm, on two classification tasks is studied theoretically. In the first example the task is defined by a restricted two-layer network, a committee machine, and in the second the task is defined by the so-called prototype problem. The architecture of the learning machine, in both cases, ...

This study integrates log-derived empirical formulas and the concept of the committee machine to develop an improved model for predicting permeability. A set of three empirical formulas, such as the Wyllie–Rose, Coates–Dumanoir, and porosity models to correlate reservoir well-logging information with measured core permeability, are used as expert members in a committee machine. A committee mach...

In many data mining applications we are given a set of training examples and asked to construct a regression machine or a classifier that has low prediction error or low error rate on new examples, respectively. An important issue is speed especially when there are large amounts of data. We show how both classification and prediction error can be reduced by using boosting techniques to implemen...

In this chapter, we describe some of the most important architectures and algorithms for committee machines. We discuss three reasons for using committee machines. The first reason is that a committee can achieve a test set performance unobtainable by a single committee member. As typical representative approaches, we describe simple averaging, bagging and boosting. Secondly with committee mach...

We have developed an adaptive handwriting recognizer for isolated Latin characters in which the adaptive behavior is based on the Dynamically Expanding Context (DEC) algorithm. In our current system, the outputs of a set of static classifiers are combined in a committee machine, whose rules are adapted. Every misclassified character gives rise to adding a new DEC rule to the rule set of the com...

Nowadays, the automated text classification has witnessed special importance due to the increasing availability of documents in digital form and ensuing need to organize them. Although this problem is in the Information Retrieval (IR) field, the dominant approach is based on machine learning techniques. Approaches based on classifier committees have shown a better performance than the others. I...

s 0 is encoded into a codeword y0 using a tree-like committee machine (committee tree) or a tree-like parity machine (parity tree). Based on these architectures, several schemes featuring monotonic or non-monotonic units are introduced. The codeword y0 is then transmitted via a Binary Asymmetric Channel (BAC) where it is corrupted by noise. The analytical performance of these schemes is investi...