Journal ArticleDOI
Methods of combining multiple classifiers and their applications to handwriting recognition
Lei Xu,Adam Krzyżak,Ching Y. Suen +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. >read more
Citations
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Proceedings ArticleDOI
Hybrid Classifier Systems for Intrusion Detection
Te-Shun Chou,Tsung-Nan Chou +1 more
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
Ryan M. Robinson,Hyungtae Lee,Michael J. McCourt,Amar R. Marathe,Heesung Kwon,Chau Ton,William D. Nothwang +6 more
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
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
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.