نتایج جستجو برای: classifier combination

تعداد نتایج: 419428  

Journal: :IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics : a publication of the IEEE Systems, Man, and Cybernetics Society 2002
Ludmila I. Kuncheva

This paper presents a combination of classifier selection and fusion by using statistical inference to switch between the two. Selection is applied in those regions of the feature space where one classifier strongly dominates the others from the pool [called clustering-and-selection or (CS)] and fusion is applied in the remaining regions. Decision templates (DT) method is adopted for the classi...

Journal: :iranian journal of fuzzy systems 2014
p. moallem n. razmjooy b. s. mousavi

potato image segmentation is an important part of image-based potato defect detection. this paper presents a robust potato color image segmentation through a combination of a fuzzy rule based system, an image thresholding based on genetic algorithm (ga) optimization and morphological operators. the proposed potato color image segmentation is robust against variation of background, distance and ...

Journal: :EURASIP J. Adv. Sig. Proc. 2004
Jian Huang Pong C. Yuen Jian-Huang Lai Chun-hung Li

The combining classifier approach has proved to be a proper way for improving recognition performance in the last two decades. This paper proposes to combine local and global facial features for face recognition. In particular, this paper addresses three issues in combining classifiers, namely, the normalization of the classifier output, selection of classifier(s) for recognition, and the weigh...

2015
Qiuchi Li Qiyu Zhi Miao Li

This paper describes our system (MSIIP THU) used for Topic-Based Chinese Message Polarity Classification Task in SIGHAN-8. In our system, a lexiconbased classifier and a statistical machine learning-based classifier are built up, followed by a linear combination of these two models. The overall performance of the proposed framework ranks in the middle of all terms participating in the task.

2004
Zheng Zhang Shuigeng Zhou Aoying Zhou

In this paper, we introduce Sequential Classifiers Combination (SCC) into text categorization to improve both the classification effectiveness and classification efficiency of the combined individual classifiers. We apply two classifiers sequentially for experimental study, where the first classifier (called filtering classifier) is used to generate candidate categories for the test document an...

Journal: :NMR in biomedicine 2012
P Tiwari S Viswanath J Kurhanewicz A Sridhar A Madabhushi

Recently, both Magnetic Resonance (MR) Imaging (MRI) and Spectroscopy (MRS) have emerged as promising tools for detection of prostate cancer (CaP). However, due to the inherent dimensionality differences in MR imaging and spectral information, quantitative integration of T(2) weighted MRI (T(2)w MRI) and MRS for improved CaP detection has been a major challenge. In this paper, we present a nove...

Journal: :Information Fusion 2002
Ludmila I. Kuncheva Marina Skurichina Robert P. W. Duin

In classifier combination, it is believed that diverse ensembles have a better potential for improvement on the accuracy than nondiverse ensembles. We put this hypothesis to a test for two methods for building the ensembles: Bagging and Boosting, with two linear classifier models: the nearest mean classifier and the pseudo-Fisher linear discriminant classifier. To estimate diversity, we apply n...

2005
Yu Su Shiguang Shan Bo Cao Xilin Chen Wen Gao

Gabor features has been recognized as one of the most successful representation methods, such as Elastic Graph Matching, Gabor Fisher Classifier, and AdaBoost Gabor Fisher Classifier. One of the key issues in using Gabor features is how to efficiently reduce its high dimensionality. This paper proposes a multiple Fisher classifiers combination approach based on re-grouping Gabor features select...

2010
Ali Zulfiqar Aslam Muhammad Ana María Martínez Enríquez Gonzalo Escalada-Imaz

Every feature extraction and modeling technique of voice/speech is not suitable in all type of environments. In many real life applications, it is not possible to use all type of feature extraction and modeling techniques to design a single classifier for speaker identification tasks because it will make the system complex. So instead of exploring more techniques or making the system complex it...

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