Diversity between Neural Networks and Decision Trees for Building Multiple Classifier Systems

Author(s):  
Wenjia Wang ◽  
Phillis Jones ◽  
Derek Partridge
2014 ◽  
pp. 210-216
Author(s):  
Hirotaka Inoue ◽  
Kyoshiro Sugiyama

R ecently, mul tiple classifier systems have been used for practical applications to improve classification accuracy. Self-generating neural networks are one of the most suitable base-classifiers for multiple classifier systems because of their simple settings and fast learning ability. However, the computation cost of the multiple classifier system based on self-generating neural networks increases in proportion to the numbers of self-gene rating neural networks. In this paper, w e propose a novel prunin g method for efficient classification and we call this model a self-organizing neural grove. Experiments have been conducted to compare the self-organizing neural grove with bagging and the self-organizing neural grove with boosting, and support vector machine. The results show that the self-organizing neural grove can improve its classification accuracy as well as reducing the computation cost.


Author(s):  
SIMON GÜNTER ◽  
HORST BUNKE

Handwritten text recognition is one of the most difficult problems in the field of pattern recognition. In this paper, we describe our efforts towards improving the performance of state-of-the-art handwriting recognition systems through the use of classifier ensembles. There are many examples of classification problems in the literature where multiple classifier systems increase the performance over single classifiers. Normally one of the two following approaches is used to create a multiple classifier system. (1) Several classifiers are developed completely independent of each other and combined in a last step. (2) Several classifiers are created out of one prototype classifier by using so-called classifier ensemble creation methods. In this paper an algorithm which combines both approaches is introduced and it is used to increase the recognition rate of a hidden Markov model (HMM) based handwritten word recognizer.


Author(s):  
ROMAN BERTOLAMI ◽  
HORST BUNKE

Current multiple classifier systems for unconstrained handwritten text recognition do not provide a straightforward way to utilize language model information. In this paper, we describe a generic method to integrate a statistical n-gram language model into the combination of multiple offline handwritten text line recognizers. The proposed method first builds a word transition network and then rescores this network with an n-gram language model. Experimental evaluation conducted on a large dataset of offline handwritten text lines shows that the proposed approach improves the recognition accuracy over a reference system as well as over the original combination method that does not include a language model.


Author(s):  
Mario Barbareschi ◽  
Salvatore Del Prete ◽  
Francesco Gargiulo ◽  
Antonino Mazzeo ◽  
Carlo Sansone

Sign in / Sign up

Export Citation Format

Share Document