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Journal ArticleDOI

Methods of combining multiple classifiers and their applications to handwriting recognition

Lei Xu, +2 more
- Vol. 22, Iss: 3, pp 418-435
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TLDR
On applying these methods to combine several classifiers for recognizing totally unconstrained handwritten numerals, the experimental results show that the performance of individual classifiers can be improved significantly.
Abstract
Possible solutions to the problem of combining classifiers can be divided into three categories according to the levels of information available from the various classifiers. Four approaches based on different methodologies are proposed for solving this problem. One is suitable for combining individual classifiers such as Bayesian, k-nearest-neighbor, and various distance classifiers. The other three could be used for combining any kind of individual classifiers. On applying these methods to combine several classifiers for recognizing totally unconstrained handwritten numerals, the experimental results show that the performance of individual classifiers can be improved significantly. For example, on the US zipcode database, 98.9% recognition with 0.90% substitution and 0.2% rejection can be obtained, as well as high reliability with 95% recognition, 0% substitution, and 5% rejection. >

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Citations
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Proceedings ArticleDOI

Hybrid Classifier Systems for Intrusion Detection

TL;DR: A hybrid design for intrusion detection that combines anomaly detection with misuse detection is described that effectively generates a more accurate intrusion detection model on detecting both normal usages and malicious activities.
Book ChapterDOI

Comparison and combination of statistical and Neural Network algorithms for remote-sensing image classification

TL;DR: Results on a multi-sensor remote-sensing data set point out that the use of classifiers ensembles can constitute a valid alternative to the development of new classification algorithms “more complex” than the present ones.
Journal ArticleDOI

Algorithmic fusion of gene expression profiling for diffuse large B-cell lymphoma outcome prediction

TL;DR: An algorithmic fusion approach is presented for extracting genes that are predictive to clinical outcomes (survival-fatal) of diffuse large B-cell lymphoma on a set of microarray data for gene expression profiling.
Proceedings ArticleDOI

Human-autonomy sensor fusion for rapid object detection

TL;DR: Fusion of human electroencephalography and button-press responses from rapid serial visual presentation experiments are fused with outputs from trained object detection algorithms and it is demonstrated that fusion of these human classifiers with computer-vision-based detectors improves object detection accuracy.
Dissertation

Optimizing Spectral Feature Based Text-Independent Speaker Recognition

Tomi Kinnunen
TL;DR: In this thesis, the subcomponents of text-independent speaker recognition are studied, and several improvements are proposed for achieving better accuracy and faster processing.
References
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Book

Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.
Book

A mathematical theory of evidence

Glenn Shafer
TL;DR: This book develops an alternative to the additive set functions and the rule of conditioning of the Bayesian theory: set functions that need only be what Choquet called "monotone of order of infinity." and Dempster's rule for combining such set functions.
Journal ArticleDOI

Statistical and structural approaches to texture

TL;DR: This survey reviews the image processing literature on the various approaches and models investigators have used for texture, including statistical approaches of autocorrelation function, optical transforms, digital transforms, textural edgeness, structural element, gray tone cooccurrence, run lengths, and autoregressive models.
Journal ArticleDOI

An introduction to hidden Markov models

TL;DR: The purpose of this tutorial paper is to give an introduction to the theory of Markov models, and to illustrate how they have been applied to problems in speech recognition.