نتایج جستجو برای: one method named supervised fuzzy c

تعداد نتایج: 4192688  

2013
Mousa nazari Jamshid Shanbehzadeh

Semi-supervised learning is somewhere between unsupervised and supervised learning. In fact, most semi-supervised learning strategies are based on extending either unsupervised or supervised learning to include additional information typical of the other learning paradigm. Constraint fuzzy c-means a novel semi-supervised fuzzy c-means algorithm proposed by Li et al [1]. Constraint FCM like FCM ...

2004
Daoqiang Zhang Keren Tan Songcan Chen

This paper presents a semi-supervised kernel-based fuzzy c-means algorithm called S2KFCM by introducing semi-supervised learning technique and the kernel method simultaneously into conventional fuzzy clustering algorithm. Through using labeled and unlabeled data together, S2KFCM can be applied to both clustering and classification tasks. However, only the latter is concerned in this paper. Expe...

پایان نامه :وزارت علوم، تحقیقات و فناوری - دانشگاه علامه طباطبایی - دانشکده اقتصاد 1389

this thesis is a study on insurance fraud in iran automobile insurance industry and explores the usage of expert linkage between un-supervised clustering and analytical hierarchy process(ahp), and renders the findings from applying these algorithms for automobile insurance claim fraud detection. the expert linkage determination objective function plan provides us with a way to determine whi...

Journal: :CLEI Electron. J. 2010
Fabio J. J. Santos Heloisa A. Camargo

One of the techniques used to support decisions in uncertain environments is the Fuzzy TOPSIS method. However, from crisp data, this method considers only one fuzzy set in their analysis, besides being a strictly mathematical optimization technique. This article proposes extensions to the original Fuzzy TOPSIS, exploring two distinct versions: to increase the method with the necessary resources...

2003
Robert Munro Daren Ler Jon Patrick

This paper presents a named entity classification system that utilises both orthographic and contextual information. The random subspace method was employed to generate and refine attribute models. Supervised and unsupervised learning techniques used in the recombination of models to produce the final results.

2014
S.Shalini R.Raja

In semi supervised clustering is one of the major tasks and aims at grouping the data objects into meaningful classes (clusters) such that the similarity of objects within clusters is maximized and the similarity of objects between clusters is minimized. The dataset sometimes may be in mixed nature that is it may consist of both numeric and categorical type of data. Naturally these two types of...

2016
Xiangjian Chen Di Li Hongmei Li

This paper presents a new clustering algorithm named improved type-2 possibilistic fuzzy c-means (IT2PFCM) for fuzzy segmentation of magnetic resonance imaging, which combines the advantages of type 2 fuzzy set, the fuzzy c-means (FCM) and Possibilistic fuzzy c-means clustering (PFCM). First of all, the type 2 fuzzy is used to fuse the membership function of the two segmentation algorithms (FCM...

Journal: :iranian journal of fuzzy systems 2014
mohsen zeinalkhani mahdi eftekhari

fuzzy decision tree (fdt) classifiers combine decision trees with approximate reasoning offered by fuzzy representation to deal with language and measurement uncertainties. when a fdt induction algorithm utilizes stopping criteria for early stopping of the tree's growth, threshold values of stopping criteria will control the number of nodes. finding a proper threshold value for a stopping crite...

Journal: :Pattern Recognition 1996
Amine Bensaid Lawrence O. Hall James C. Bezdek Laurence P. Clarke

All clustering algorithms process unlabeled data and, consequently, suffer from two problems: (P1) choosing and validating the correct number of clusters; and (P2) insuring that algorithmic labels correspond to meaningful physical labels. Clustering algorithms such as hard and fuzzy c-Means, based on optimizing sums of squared errors objective functions, suffer from a third problem: (P3) a tend...

2014
S. Vatlin

A possible interpretation, in terms offuzzy classification models (fuzzy classifiers), of one pf the general principles of choosing a scientific theory a consistency principle is considered. Supervised self-guessing fuzzy classifiers are determined. A theorem on character of restrictions induced on a set of supervised fuzzy classifiers by a self-guessing requirement is proved. FeaSible alternat...

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