Ensemble learning aims at combining several slightly different learners to construct stronger learner. Ensemble of a well selected subset of learners would outperform than ensemble of all. However, the well studied accuracy / diversity ensemble pruning framework would lead to over fit of training data, which results a target learner of relatively low generalization ability. We propose to ensemb...

For the World Wide Web, the Transmission Control Protocol (TCP) and the HyperText Transfer Protocol (HTTP) are two important protocols. However, interactions between these two protocols, combined with the bandwidth asymmetry of network access technologies such as ADSL, can lead to inefficient HTTP/TCP performance. This paper investigates the effects of bandwidth asymmetry on Ensemble-TCP, a pro...

An ensemble is a collection of related datasets. Each dataset, or member, of an ensemble is normally large, multidimensional, and spatio-temporal. Ensembles are used extensively by scientists and mathematicians, for example, by executing a simulation repeatedly with slightly different input parameters and saving the results in an ensemble to see how parameter choices affect the simulation. To d...

Ensemble classification is an emerging approach to land cover mapping whereby the final classification output is a result of a ‘consensus’ of classifiers. Intuitively, an ensemble system should consist of base classifiers which are diverse i.e. classifiers whose decision boundaries err differently. In this paper ensemble feature selection is used to impose diversity in ensembles. The features o...

It is well known that ensembles of predictors produce better accuracy than a single predictor provided there is diversity in the ensemble. This diversity manifests itself as disagreement or ambiguity among the ensemble members. In this paper we focus on ensembles of classifiers based on different feature subsets and we present a process for producing such ensembles that emphasizes diversity (am...

A new ensemble of low-density parity-check (LDPC) codes for correcting a solid burst erasure is proposed. This ensemble is an instance of a combined matrix ensemble obtained by concatenating some LDPC matrices. We derive a new bound on the critical minimum span ratio of stopping sets for the proposed code ensemble by modifying the bound for ordinary code ensemble. By calculating this bound, we ...

On-line learning of a hierarchical learning model is studied by a method from statistical mechanics. In our model a student of a simple perceptron learns from not a true teacher directly, but ensemble teachers who learn from the true teacher with a perceptron learning rule. Since the true teacher and the ensemble teachers are expressed as non-monotonic perceptron and simple ones, respectively, ...

A novel method of introducing diversity into ensemble learning predictors for regression problems is presented. The proposed method prunes the ensemble while simultaneously training, as part of the same learning process. Here not all members of the ensemble are trained, but selectively trained, resulting in a diverse selection of ensemble members that have strengths in different parts of the tr...

1 Lecture 1 3 1.1 Bayesian Inference and Estimators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.1.1 The Bayes formula . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.1.2 Estimators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2 A toy example in denoising . . . . . . . . . . . . . . . . . . . . ....

To learn any problem, many classifiers have been introduced so far. Each of these classifiers has many strengths (positive aspects) and weaknesses (negative aspects) that make it suitable for some specific problems. But there is no powerful solution to indicate which classifier is the best classifier (or at least a good one) for a special problem. Fortunately the ensemble learning provides us w...