This chapter gives a tutorial introduction to Ensemble Learning, a recently developed Bayesian method. For many problems it is intractable to perform inferences using the true posterior density over the unknown variables. Ensemble Learning allows the true posterior to be approximated by a simpler approximate distribution for which the required inferences are tractable.

Ensemble Learning refers to the procedures employed to train multiple learning machines and combine their outputs, treating them as a “committee” of decision makers. The principle is that the committee decision, with individual predictions combined appropriately, should have better overall accuracy, on average, than any individual committee member. Numerous empirical and theoretical studies hav...

It is a common practice when problem solving to seek the advice of others as to what decision to make next. One may seek advice from a single trusted colleague, but commonly one seeks advice frommultiple sources, weighing the advice from each before making a decision (Polikar 2006). This behavior is mimicked by ensemble methods for machine learning which combine outputs from multiple independen...

Student performance prediction is a great area of concern for educational institutions to prevent their students from failure by providing necessary support and counseling to complete their degree successfully. The scope of this research is to examine the accuracy of the ensemble techniques for predicting the student's academic performance, particularly for four year engineering graduate p...

In this paper, we propose novel recurrent architectures for Genetic Programming (GP) and Group Method of Data Handling (GMDH) to predict software reliability. The effectiveness of the models is compared with that of well-known machine learning techniques viz. Multiple Linear Regression (MLR), Multivariate Adaptive Regression Splines (MARS), Backpropagation Neural Network (BPNN), Counter Propaga...

Linear regression and regression tree models are among the most known regression models used in the machine learning community and recently many researchers have examined their sufficiency in ensembles. Although many methods of ensemble design have been proposed, there is as yet no obvious picture of which method is best. One notable successful adoption of ensemble learning is the distributed s...

Supervised polarity classification systems are typically domain-specific. Building these systems involves the expensive process of annotating a large amount of data for each domain. A potential solution to this corpus annotation bottleneck is to build unsupervised polarity classification systems. However, unsupervised learning of polarity is difficult, owing in part to the prevalence of sentime...

the wisdom of crowds, an innovative theory described in social science, claims that the aggregate decisions made by a group will often be better than those of its individual members if the four fundamental criteria of this theory are satisfied. this theory used for in clustering problems. previous researches showed that this theory can significantly increase the stability and performance of lea...

Multilayer Perceptrons (MLPs) have been proven to be an effective way to solve classification tasks. A major concern in their use is the difficulty to define the proper network for a specific application, due to the sensitivity to the initial conditions and overfitting and underfitting problems which limit their generalization capability. Moreover, time and hardware constraints may seriously re...