This chapter presents a framework for multi-biometric match score fusion when non-ideal conditions cause conflict in the results of different unimodal biometrics classifiers. The proposed framework uses belief function theory to effectively fuse the match scores and density estimation technique to compute the belief assignments. Fusion is performed using belief models such as Transferable Belie...

New challenges in knowledge extraction include interpreting and classifying data sets while simultaneously considering related information to confirm results or identify false positives. We discuss a data fusion algorithmic framework targeted at this problem. It includes separate base classifiers for each data type and a fusion method for combining the individual classifiers. The fusion method ...

Pattern recognition systems are widely used in a host of different fields. Due to some reasons such as lack of knowledge about a method based on which the best classifier is detected for any arbitrary problem, and thanks to significant improvement in accuracy, researchers turn to ensemble methods in almost every task of pattern recognition. Classification as a major task in pattern recognition,...

We present an information fusion approach to designing a semantics-sensitive image retrieval system. Our approach is based on the three different types of classifiers, which extract and provide semantic cues of image regions. This local information from the proposed classifiers is fused together to generate the semantic labels of images. The experimental results show that our information fusion...

This study compares the classification ability of various fusion algorithms (average, majority vote, median, max/min) when individual classifiers are potentially correlated. A logistic transformation of multivariate normal distribution (MVN) is used to generate the posterior probability estimates, assuring that the probability exists between 0 and 1. With varying parameters of MVN and number of...

In this paper, an experimental comparison between fixed and trained fusion rules for multimodal personal identity verification is reported. We focused on the behaviour of the considered fusion methods for ensembles of classifiers exhibiting significantly different performance, as this is one of the main characteristics of multimodal biometrics systems. The experiments were carried out on the XM...

In this paper we propose a new technique to enhance the performance of spoken dialogue systems by means of recognising users’ emotional states. The technique employs two fusion modules that combine emotional predictions. The former employs a number of fusion methods to combine predictions made by classifiers that deal with different types of information regarding each sentence uttered by the us...

of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy ROBUST KERNEL METHODS IN CONTEXT-DEPENDENT FUSION By Gyeongyong Heo December 2009 Chair: Paul Gader Major: Computer Engineering Combining classifiers, a common way to improve the performance of classification, has gained popularity in re...