user-generated medical messages on internet contain extensive information related to adverse drug reactions (adrs) and are known as valuable resources for post-marketing drug surveillance. the aim of this study was to find an effective method to identify messages related to adrs automatically from online user reviews.we conducted experiments on online user reviews using different feature set an...

sleep stages classification is one of the most important methods for diagnosis in psychiatry and neurology. in this paper, a combination of three kinds of classifiers are proposed which classify the eeg signal into five sleep stages including awake, n-rem (non-rapid eye movement) stage 1, n-rem stage 2, n-rem stage 3 and 4 (also called slow wave sleep), and rem. twenty-five all night recordings...

Combining classifiers is to join the strengths of different classifiers to improve the classification performance. Using rules to combine the outputs of different classifiers is the basic structure of classifier combination. Fusing models from different kernel machine classifiers is another strategy for combining models called kernel combination. Although classifier combination and kernel combi...

Bayesian model averaging linearly mixes the probabilistic predictions of multiple models, each weighted by its posterior probability. This is the coherent Bayesian way of combining multiple models only under certain restrictive assumptions, which we outline. We explore a general framework for Bayesian model combination (which differs from model averaging) in the context of classification. This ...

ÐFingerprint classification is an important indexing method for any large scale fingerprint recognition system or database as a method for reducing the number of fingerprints that need to be searched when looking for a matching print. Fingerprints are generally classified into broad categories based on global characteristics. This paper describes novel methods of classification using hidden Mar...

Classifier combinations are effective techniques for difficult pattern recognition problems such as speech recognition where the combination of differently trained classifiers can produce a more robust phoneme classification on noisy datasets. In this paper we investigate traditional linear combination schemes (e.g. arithmetic mean and least squares methods), and propose a new combiner based on...

A vital aspect of the classification based model construction process is calibration scoring function. One weaknesses that it does not take into account information about relative positions recognized objects in feature space. To alleviate this limitation, paper, we propose a novel concept calculating function on distance object from decision boundary and its to class centroid. An important pro...

Classifier combination methods have proved to be an effective tool to increase the performance of pattern recognition applications. In this chapter we review and categorize major advancements in this field. Despite a significant number of publications describing successful classifier combination implementations, the theoretical basis is still missing and achieved improvements are inconsistent. ...

In this course project, I will attempt to write up a survey of combination methods used in speech recognition. I will attempt to evaluate them with a more general view of classifier combining, and also consider the usability of some adaptive combination methods that have used in my previous research within the domain of speech recognition. Thus the project consists of two parts, a survey into e...