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 large open pit mines prediction of Peak Particle Velocity (PPV) provides useful information for safe blasting. At Sungun Copper Mine (SCM), some unstable rock slopes facing to valuable industrial facilities are both expose to high intensity daily blasting vibrations, threatening their safty. So, controlling PPV by developing accurate predictors is essential. Hence, this study proposes improv...

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...

Background and Objectives: Stock price prediction has become one of the interesting and also challenging topics for researchers in the past few years. Due to the non-linear nature of the time-series data of the stock prices, mathematical modeling approaches usually fail to yield acceptable results. Therefore, machine learning methods can be a promising solution to this problem. Methods: In this...

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...

Human ttributions of intelligence in others reflect heuristic judgments of superficial qualities rather than systematic analysis of competence. This article reviews the literature on attribution of intelligence in humans and machines, examines the public controversy over human intelligence, mid extrapolates to the subject of machine intelligence. The possible effects on public policy ale discus...