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
Reads0
Chats0
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
More filters
Journal ArticleDOI
Collaborative Fusion for Distributed Target Classification Using Evidence Theory in IOT Environment
TL;DR: The inner reliability is applied to transfer the local hard decision into rational soft decision, and the relative reliability is utilized to decrease the influence of conflicting soft decisions by making full use of the evidential discounting operation.
Journal ArticleDOI
Method of classifier selection using the genetic approach
Konrad Jackowski,Michal Wozniak +1 more
TL;DR: A novel machine learning algorithm used for training a compound classifier system that consists of a set of area classifiers that outperforms each elementary classifier as well as simple voting.
Journal ArticleDOI
Fusion of hyperspectral and LIDAR data using decision template-based fuzzy multiple classifier system
TL;DR: Fuzzy MCS on HSI and LIDAR data provide interesting conclusions on the effectiveness and potentialities of the joint use of these two data.
Journal ArticleDOI
Handwritten Chinese character recognition by metasynthetic approach
TL;DR: Two integration approaches for handwritten Chinese character recognition based on a Linear Model and a Network Integration based on Supervised Learning succeed in automatically acquiring the parameters of the integrated systems by supervised learning which is very important for the large number of classes of pattern recognition problems.
Journal ArticleDOI
Moderating k-NN Classifiers
Fuad M. Alkoot,Josef Kittler +1 more
TL;DR: The proposed moderation method improves the performance of the multiple classifier system significantly and can be minimised by marginalising the k-NN estimates using the Bayesian prior.
References
More filters
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.