Topic Modelling and Clustering of Disaster-Related Tweets using Bilingual Latent Dirichlet Allocation and Incremental Clustering Algorithm with Support Vector Machines for Need Assessment

2021 ◽  
Author(s):  
Lady Angelica Buen Guerzo ◽  
Hans Aaron O. Kilkenny ◽  
Raphael Noel D. Osorio ◽  
Andrei Hart E. Villegas ◽  
Charmaine S. Ponay
Keyword(s):  
Support Vector Machines ◽  
Clustering Algorithm ◽  
Latent Dirichlet Allocation ◽  
Support Vector ◽  
Topic Modelling ◽  
Incremental Clustering ◽  
Need Assessment ◽  
Vector Machines ◽  
Dirichlet Allocation

scholarly journals An Algebraic Approach to Clustering and Classification with Support Vector Machines

Mathematics ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 128
Author(s):  
Güvenç Arslan ◽  
Uğur Madran ◽  
Duygu Soyoğlu
Keyword(s):  
Support Vector Machines ◽  
Clustering Algorithm ◽  
Algebraic Approach ◽  
Real Data ◽  
Training Data ◽  
Support Vector ◽  
Intermediate Step ◽  
Data Set ◽  
Vector Machines ◽  
Clustering And Classification

In this note, we propose a novel classification approach by introducing a new clustering method, which is used as an intermediate step to discover the structure of a data set. The proposed clustering algorithm uses similarities and the concept of a clique to obtain clusters, which can be used with different strategies for classification. This approach also reduces the size of the training data set. In this study, we apply support vector machines (SVMs) after obtaining clusters with the proposed clustering algorithm. The proposed clustering algorithm is applied with different strategies for applying SVMs. The results for several real data sets show that the performance is comparable with the standard SVM while reducing the size of the training data set and also the number of support vectors.

Study on PMV Index Forecasting Method Based on Fuzzy C-Means Clustering

2011 ◽  
Vol 383-390 ◽  
pp. 925-930
Author(s):  
Chun Cheng Zhang ◽  
Xiang Guang Chen ◽  
Yuan Qing Xu
Keyword(s):  
Support Vector Machines ◽  
Clustering Algorithm ◽  
Large Data ◽  
Support Vector ◽  
Data Sets ◽  
Forecasting Accuracy ◽  
Fuzzy C Means ◽  
Forecasting Method ◽  
Vector Machines ◽  
Fuzzy C Means Clustering

In order to improve the forecasting accuracy of indoor thermal comfort, the basic principle of fuzzy c-means clustering algorithm (FCM) and support vector machines (SVM) is analyzed. A kind of SVM forecasting method based on FCM data preprocess is proposed in this paper. The large data sets can be divided into multiple mixed groups and each group is represented by a single regression model using the proposed method. The support vector machines based on fuzzy c-means clustering algorithm (FCM+SVM) and the BP neural network based on fuzzy c-means clustering algorithm (FCM+BPNN) are respectively applied to forecast PMV index. The experimental results demonstrate that the FCM+SVM method has better forecasting accuracy compared with FCM+BPNN method.

Soft Biometrics Classification Using Denoising Convolutional Autoencoders and Support Vector Machines

2018 ◽  
Author(s):  
Nelson Marcelo Romero Aquino ◽  
Matheus Gutoski ◽  
Leandro Takeshi Hattori ◽  
Heitor Silvério Lopes
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
Soft Biometrics ◽  
Vector Machines
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