نتایج جستجو برای: one method named supervised fuzzy c
تعداد نتایج: 4192688 فیلتر نتایج به سال:
diabetic retinopathy is one of the most important reasons of blindness which causes serious damage in the retina. the aim of this research is to detect one lesions of the retina, named exudates automatically with image processing techniques. preprocessing is the first step of proposed algorithm. after preprocessing, the optic disc was detected and removed from the retinal image due to the same ...
Fuzzy logic systems are promising for efficient obstacle avoidance. However, it is difficult to maintain the correctness, consistency, and completeness of a fuzzy rule base constructed and tuned by a human expert. A reinforcement learning method is capable of learning the fuzzy rules automatically. However, it incurs a heavy learning phase and may result in an insufficiently learned rule base d...
Hidden Markov Model is a popular statisical method that is used in continious and discrete speech recognition. The probability density function of observation vectors in each state is estimated with discrete density or continious density modeling. The performance (in correct word recognition rate) of continious density is higher than discrete density HMM, but its computation complexity is very ...
We propose a learning approach to designing fuzzy controllers based on the B-spline model. Unlike other normalised parameterised set functions for deening fuzzy sets, B-spline basis functions do not necessarily span from membership values zero to one, but possess the property \partition of unity". B-spline basis functions can be automatically determined after each input is partitioned. Learning...
The definition of the Fuzzy Rule Base is one of the most important and difficult tasks when designing Fuzzy Systems. This paper discusses the results of two different hybrid methods, previously investigated, for the automatic generation of fuzzy rules from numerical data. One of the methods, named DoC-based, proposes the creation of Fuzzy Rule Bases using genetic algorithms in association with ...
Fuzzy pattern matching technique represents a group of fuzzy methods for supervised fuzzy pattern recognition. These methods build a prototype for each feature and combine the partial estimations of each prototype by a fusion operator. One of the major problems of this technique is that it is not able to model the dependencies between features, and nowadays there is no heuristic in the literatu...
Although many classification methods take advantage of fuzzy sets theory, the same cannot be said for feature reduction methods. In this paper we explore ideas related to the use of fuzzy sets and we propose a novel fuzzy feature selection method tailored for the Regularized Generalized Eigenvalue Classifier (ReGEC). The method provides small and robust subsets of features that can be used for ...
Named entity recognition has achieved remarkable success on benchmarks with high-quality manual annotations. Such annotations are labor-intensive and time-consuming, thus unavailable in real-world scenarios. An emerging interest is to generate low-cost but noisy labels via distant supervision, hence label learning algorithms demand. In this paper, a unified self-adaptive framework termed Self-A...
This paper presents texture segmentation concept using supervised method in contextual clustering and fuzzy logic. The data set used is the textile textures. The image is split into 3 X 3
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