Proceedings ArticleDOI
Creation of classifier ensembles for handwritten word recognition using feature selection algorithms
Simon Günter,Horst Bunke +1 more
- pp 183-188
TLDR
New methods for the creation of classifier ensembles based on feature selection algorithms are introduced, and are evaluated and compared to existing approaches in the context of handwritten word recognition, using a hidden Markov model recognizer as basic classifier.Abstract:
The study of multiple classifier systems has become an area of intensive research in pattern recognition. Also in handwriting, recognition, systems combining several classifiers have been investigated. In the paper new methods for the creation of classifier ensembles based on feature selection algorithms are introduced. These new methods are evaluated and compared to existing approaches in the context of handwritten word recognition, using a hidden Markov model recognizer as basic classifier.read more
Citations
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MonographDOI
Combining Pattern Classifiers
TL;DR: This combining pattern classifiers methods and algorithms helps people to enjoy a good book with a cup of coffee in the afternoon, instead they cope with some harmful virus inside their computer.
Proceedings ArticleDOI
Recognition of cursive Roman handwriting: past, present and future
TL;DR: The state of the art in off-line Roman cursive handwriting recognition is reviewed, recent trends are analyzed, and challenges for future research in this field are identified.
Journal ArticleDOI
Feature selection algorithms for the generation of multiple classifier systems and their application to handwritten word recognition
Simon Günter,Horst Bunke +1 more
TL;DR: New methods for the creation of classifier ensembles based on feature selection algorithms are introduced, and are evaluated and compared to existing approaches in the context of handwritten word recognition, using a hidden Markov model recognizer as basic classifier.
Book ChapterDOI
Feature Selection for Ensembles Using the Multi-Objective Optimization Approach
TL;DR: An ensemble feature selection approach based on a hierarchical multi-objective genetic algorithm based on the “overproduce and choose” paradigm brings compelling improvements when classifiers have to work with very low error rates.
Book ChapterDOI
Towards Bridging the Gap between Statistical and Structural Pattern Recognition: Two New Concepts in Graph Matching
TL;DR: It is argued that with these new concepts various well-established techniques from statistical pattern recognition become applicable in the structural domain, particularly to graph representations, including k-means clustering, vector quantization, and Kohonen maps.
References
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A tutorial on hidden Markov models and selected applications in speech recognition
TL;DR: In this paper, the authors provide an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and give practical details on methods of implementation of the theory along with a description of selected applications of HMMs to distinct problems in speech recognition.
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Principal Component Analysis
TL;DR: In this article, the authors present a graphical representation of data using Principal Component Analysis (PCA) for time series and other non-independent data, as well as a generalization and adaptation of principal component analysis.
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Bagging predictors
TL;DR: Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy.
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Statistical pattern recognition: a review
TL;DR: The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system and identify research topics and applications which are at the forefront of this exciting and challenging field.
Book
Tabu Search
Fred Glover,Manuel Laguna +1 more
TL;DR: This book explores the meta-heuristics approach called tabu search, which is dramatically changing the authors' ability to solve a host of problems that stretch over the realms of resource planning, telecommunications, VLSI design, financial analysis, scheduling, spaceplanning, energy distribution, molecular engineering, logistics, pattern classification, flexible manufacturing, waste management,mineral exploration, biomedical analysis, environmental conservation and scores of other problems.