Multiple Classifier Boosting and Tree-Structured Classifiers

2013 ◽  
pp. 163-196 ◽  
Author(s):  
Tae-Kyun Kim ◽  
Roberto Cipolla
Keyword(s):  
Multiple Classifier

scholarly journals A Research of Speech Emotion Recognition Based on Deep Belief Network and SVM

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Chenchen Huang ◽  
Wei Gong ◽  
Wenlong Fu ◽  
Dongyu Feng
Keyword(s):  
Feature Extraction ◽  
Emotion Recognition ◽  
Recognition Rate ◽  
Original Method ◽  
Speech Emotion Recognition ◽  
High Dimensional ◽  
Svm Classifier ◽  
Multiple Classifier System ◽  
Classifier System ◽  
Multiple Classifier

Feature extraction is a very important part in speech emotion recognition, and in allusion to feature extraction in speech emotion recognition problems, this paper proposed a new method of feature extraction, using DBNs in DNN to extract emotional features in speech signal automatically. By training a 5 layers depth DBNs, to extract speech emotion feature and incorporate multiple consecutive frames to form a high dimensional feature. The features after training in DBNs were the input of nonlinear SVM classifier, and finally speech emotion recognition multiple classifier system was achieved. The speech emotion recognition rate of the system reached 86.5%, which was 7% higher than the original method.

HANDWRITTEN WORD RECOGNITION USING CLASSIFIER ENSEMBLES GENERATED FROM MULTIPLE PROTOTYPES

2004 ◽  
Vol 18 (05) ◽  
pp. 957-974 ◽  
Author(s):  
SIMON GÜNTER ◽  
HORST BUNKE
Keyword(s):  
Handwriting Recognition ◽  
Recognition Rate ◽  
Classifier Ensembles ◽  
Multiple Classifier Systems ◽  
Classification Problems ◽  
Classifier Systems ◽  
Handwritten Text ◽  
Handwritten Text Recognition ◽  
Multiple Classifier ◽  
System 1

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.

INTEGRATION OF n-GRAM LANGUAGE MODELS IN MULTIPLE CLASSIFIER SYSTEMS FOR OFFLINE HANDWRITTEN TEXT LINE RECOGNITION

2008 ◽  
Vol 22 (07) ◽  
pp. 1301-1321 ◽  
Author(s):  
ROMAN BERTOLAMI ◽  
HORST BUNKE
Keyword(s):  
Language Model ◽  
Language Models ◽  
Combination Method ◽  
Text Line ◽  
Multiple Classifier Systems ◽  
Classifier Systems ◽  
Handwritten Text ◽  
Handwritten Text Recognition ◽  
Multiple Classifier ◽  
N Gram

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.

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