This paper proposes an unsupervised learning model for classifying named entities. This model uses a training set, built automatically by means of a small-scale named entity dictionary and an unlabeled corpus. This enables us to classify named entities without the cost for building a large hand-tagged training corpus or a lot of rules. Our model uses the ensemble of three different learning met...

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...

This paper presents an approach with ensemble classifiers using unsupervised data selection for speaker recognition. Ensemble learning is a type of machine learning that applies a combination of several weak learners to achieve an improved performance than a single learner. Based on its acoustic characteristics, the speech utterance is divided into several subsets using unsupervised data select...

Abstract—In a series of papers by Dai and colleagues [1], [2], a feature map (or kernel) was introduced for semiand unsupervised learning. This feature map is build from the output of an ensemble of classifiers trained without using the ground-truth class labels. In this critique, we analyze the latest version of this series of papers, which is called Ensemble Projections [2]. We show that the ...

We develop a framework based on Bayesian model averaging to explain how animals cope with uncertainty about contingencies in classical conditioning experiments. Traditional accounts of conditioning fit parameters within a fixed generative model of reinforcer delivery; uncertainty over the model structure is not considered. We apply the theory to explain the puzzling relationship between second-...

This paper presents a novel approach to the task of automatic music genre classification which is based on ensemble learning. Feature vectors are extracted from three 30-second music segments from the beginning, middle and end of each music piece. Individual classifiers are trained to account for each music segment. During classification, the output provided by each classifier is combined with ...

Bagging and boosting are two popular ensemble methods that achieve better accuracy than a single classifier. These techniques have limitations on massive datasets, as the size of the dataset can be a bottleneck. Voting many classifiers built on small subsets of data (“pasting small votes”) is a promising approach for learning from massive datasets. Pasting small votes can utilize the power of b...

Any books that you read, no matter how you got the sentences that have been read from the books, surely they will give you goodness. But, we will show you one of recommendation of the book that you need to read. This ensemble methods in data mining improving accuracy through combining predictions synthesis lectures on data is what we surely mean. We will show you the reasonable reasons why you ...