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 ArticleDOI
Feature Selection and Combination Criteria for Improving Accuracy in Protein Structure Prediction
Ken-Li Lin,Chun-Yuan Lin,Chuen-Der Huang,Hsiu-Ming Chang,Chiao-Yun Yang,Chin-Teng Lin,Chuan Yi Tang,D.F. Hsu +7 more
TL;DR: This paper uses a combinatorial fusion technique to facilitate feature selection and combination for improving predictive accuracy in protein structure classification and demonstrates that data fusion is a viable method for featureselection and combination in the prediction and classification of protein structure.
Journal ArticleDOI
The fusion of large scale classified side-scan sonar image mosaics
TL;DR: A unified framework for the creation of classified maps of the seafloor from sonar imagery with significant challenges in photometric correction, classification, navigation and registration, and image fusion are addressed.
Journal ArticleDOI
Active Learning Query Strategies for Classification, Regression, and Clustering: A Survey
Punit Kumar,Atul Gupta +1 more
TL;DR: This survey reviews AL query strategies for classification, regression, and clustering under the pool-based AL scenario and presents a comparative analysis of these strategies.
Journal ArticleDOI
Classifiers Combination Techniques: A Comprehensive Review
TL;DR: A criteria-based framework for multi-classifiers combination techniques and their areas of applications is presented and the lack of a well-formulated theoretical framework for analyzing the performance of combination techniques is shown to provide a fertile ground for further research.
Journal ArticleDOI
Multiple classifiers in biometrics. part 1: Fundamentals and review
TL;DR: An introduction to Multiple Classifier Systems including basic nomenclature and describing key elements: classifier dependencies, type of classifier outputs, aggregation procedures, architecture, and types of methods is provided.
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.