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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Journal Article
A Hybrid Structural/Statistical Classifier for Handwritten Farsi/Arabic Numeral Recognition.
TL;DR: Thanks to the combination of structural and statistical approaches, a complete description of the characters can be achieved thus providing significant improvements in classification performance.
Book ChapterDOI
Intrusion Detection in Computer Systems Using Multiple Classifier Systems
TL;DR: An overview of different MCS paradigms used in the intrusion detection field, and how MCS appears to be suited to the anomaly detection paradigm, where attacks are detected as anomalies when compared to a model of normal (legitimate) event patterns.
Journal ArticleDOI
GP-based secondary classifiers
Ankur Teredesai,Venu Govindaraju +1 more
TL;DR: A two-step classification strategy aimed for increasing recognition accuracy and reliability and an intuitive motivation and detailed analysis using confusion matrices between digit classes is presented to describe how this strategy leads to improved recognition performance.
Book ChapterDOI
Combining multi-resolution evidence for georeferencing Flickr images
TL;DR: It is demonstrated experimentally that the induced belief and plausibility measures are useful to determine whether there is sufficient evidence to classify the photo at a given granularity, and an adaptive method is obtained, by which photos are georeferenced at the most appropriate resolution.
Journal ArticleDOI
Combining multiple classifiers based on third-order dependency for handwritten numeral recognition
TL;DR: This work states that the probability distribution is optimally approximated by the third-order dependency and then multiple classifiers are combined by such third- order dependency approximation.
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.