Fuzzy support vector machine with a fuzzy nearest neighbor classifier for insect footprint classification

2010 ◽  
Author(s):  
Gyeongyong Heo ◽  
Reinhard Klette ◽  
Young Woon Woo ◽  
Kwang-Baek Kim ◽  
Nam Ho Kim
Keyword(s):  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Fuzzy Support Vector Machine ◽  
Nearest Neighbor Classifier ◽  
Neighbor Classifier

Application of Fuzzy Support Vector Machine in Chalky Rice Identification

2012 ◽  
Vol 507 ◽  
pp. 202-207
Author(s):  
Xiang Li ◽  
Shang Bing Gao ◽  
Ying Quan Chen
Keyword(s):  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Identification Accuracy ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Fuzzy Support Vector Machine ◽  
Machine Method ◽  
Image Detection ◽  
Sample Mean ◽  
Support Vector Machine Method

In order to improve the identification accuracy of fuzzy support vector machine for chalky rice, this paper puts forward a fuzzy support vector machine method based on fuzzy K nearest-neighbor. This method firstly gets a sample center by calculating sample mean aimed at every class sample; and then it calculates the initial membership of sample by calculating the distance between sample and center; finally, it calculates K neighbor points of each sample, calculates the membership of sample according to the fuzzy K neighbor method, and integrates the initial membership with fuzzy K neighbor membership at a certain proportion, to get the ultimate membership values of samples. Combined with image detection problems of rice, verify the validity of this method. Experiments show that this method not only can improve the accuracy of identification but also can improve its speed, with a better result than common fuzzy support vector machine.

scholarly journals Yoruba Handwritten Character Recognition using Freeman Chain Code and K-Nearest Neighbor Classifier

2018 ◽  
Vol 6 (4) ◽  
pp. 129-134 ◽  
Author(s):  
Jumoke Falilat Ajao ◽  
David Olufemi Olawuyi ◽  
Odetunji Ode Odejobi
Keyword(s):  
Character Recognition ◽  
Nearest Neighbor ◽  
Recognition System ◽  
Support Vector ◽  
Classification Algorithms ◽  
K Nearest Neighbor ◽  
Chain Code ◽  
Nearest Neighbor Classifier ◽  
Digitized Images ◽  
Neighbor Classifier

This work presents a recognition system for Offline Yoruba characters recognition using Freeman chain code and K-Nearest Neighbor (KNN). Most of the Latin word recognition and character recognition have used k-nearest neighbor classifier and other classification algorithms. Research tends to explore the same recognition capability on Yoruba characters recognition. Data were collected from adult indigenous writers and the scanned images were subjected to some level of preprocessing to enhance the quality of the digitized images. Freeman chain code was used to extract the features of THE digitized images and KNN was used to classify the characters based on feature space. The performance of the KNN was compared with other classification algorithms that used Support Vector Machine (SVM) and Bayes classifier for recognition of Yoruba characters. It was observed that the recognition accuracy of the KNN classification algorithm and the Freeman chain code is 87.7%, which outperformed other classifiers used on Yoruba characters.

Research on Hotel Customer Relationship Management System Based on the Classification Algorithm

2019 ◽  
Vol 12 (2) ◽  
pp. 68-75
Author(s):  
Zhao Weili
Keyword(s):  
Support Vector Machine ◽  
Customer Relationship Management ◽  
Management System ◽  
Nearest Neighbor ◽  
Relationship Management ◽  
Classification Algorithm ◽  
Customer Relationship ◽  
Support Vector ◽  
Customer Acquisition ◽  
Fuzzy Support Vector Machine

The hotel management relationship is a good business strategy for hotels, which can promote the development of a hotel, when a classification algorithm is applied to customer relationship management system. First, the classification algorithm is based on a support vector machine is studied, the nearest neighbor sample density is used, and the corresponding mathematical model is constructed. Second, the procedure of a classification algorithm based on fuzzy support vector machine is designed. Third, a customer acquisition plan based on a classification algorithm is analyzed. Finally, a hotel is used as the research object, and a customer acquisition analysis is carried out, and the results show that the new method has quicker training speed and higher classification correctness.

scholarly journals Integrating the Supervised Information into Unsupervised Learning

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Ping Ling ◽  
Nan Jiang ◽  
Xiangsheng Rong
Keyword(s):  
Unsupervised Learning ◽  
Nearest Neighbor ◽  
Training Data ◽  
Support Vector ◽  
Second Phase ◽  
Nearest Neighbor Classifier ◽  
Fast Training ◽  
And Performance ◽  
Domain Description ◽  
Neighbor Classifier

This paper presents an assembling unsupervised learning framework that adopts the information coming from the supervised learning process and gives the corresponding implementation algorithm. The algorithm consists of two phases: extracting and clustering data representatives (DRs) firstly to obtain labeled training data and then classifying non-DRs based on labeled DRs. The implementation algorithm is called SDSN since it employs the tuning-scaled Support vector domain description to collect DRs, uses spectrum-based method to cluster DRs, and adopts the nearest neighbor classifier to label non-DRs. The validation of the clustering procedure of the first-phase is analyzed theoretically. A new metric is defined data dependently in the second phase to allow the nearest neighbor classifier to work with the informed information. A fast training approach for DRs’ extraction is provided to bring more efficiency. Experimental results on synthetic and real datasets verify that the proposed idea is of correctness and performance and SDSN exhibits higher popularity in practice over the traditional pure clustering procedure.

Detection of Dendritic Spines Using Wavelet Packet Entropy and Fuzzy Support Vector Machine

2017 ◽  
Vol 16 (2) ◽  
pp. 116-121 ◽  
Author(s):  
Shuihua Wang ◽  
Yang Li ◽  
Ying Shao ◽  
Carlo Cattani ◽  
Yudong Zhang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Dendritic Spines ◽  
Wavelet Packet ◽  
Support Vector ◽  
Fuzzy Support Vector Machine
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