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» Using Random Forests for Handwritten Digit Recognition
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JCDL
2009
ACM
179views Education» more  JCDL 2009»
13 years 12 months ago
Disambiguating authors in academic publications using random forests
Users of digital libraries usually want to know the exact author or authors of an article. But different authors may share the same names, either as full names or as initials and...
Pucktada Treeratpituk, C. Lee Giles
ICPR
2004
IEEE
14 years 6 months ago
An Efficient Three-Stage Classifier for Handwritten Digit Recognition
This paper proposes an efficient three-stage classifier for handwritten digit recognition based on NN (Neural Network) and SVM (Support Vector Machine) classifiers. The classifica...
Dejan Gorgevik, Dusan Cakmakov
ICPR
2002
IEEE
14 years 6 months ago
Combining SVM Classifiers for Handwritten Digit Recognition
In this paper, we investigate the advantages and weaknesses of various decision fusion schemes using statistical and rule-based reasoning. The cooperation schemes are applied on t...
Dejan Gorgevik, Dusan Cakmakov
ICANN
2005
Springer
13 years 11 months ago
Handwritten Digit Recognition with Nonlinear Fisher Discriminant Analysis
Abstract. To generalize the Fisher Discriminant Analysis (FDA) algorithm to the case of discriminant functions belonging to a nonlinear, finite dimensional function space F (Nonli...
Pietro Berkes
ICDAR
2011
IEEE
12 years 5 months ago
On-line Handwritten Japanese Characters Recognition Using a MRF Model with Parameter Optimization by CRF
— This paper describes a Markov random field (MRF) model with weighting parameters optimized by conditional random field (CRF) for on-line recognition of handwritten Japanese cha...
Bilan Zhu, Masaki Nakagawa