Scene classification using support vector machines

Mandhala, Venkata Naresh; Sujatha, V.; Devi, Bali

doi:10.1109/icaccct.2014.7019421

Cited by 22 publications

(8 citation statements)

References 2 publications

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“…SVMs try to separate datasets from each other with a linear and nonlinear line. e purpose of the SVM algorithm is to be able to distinguish between the data with the minimum error [34]. Gaussian or radial basis function (RBF) kernel (rbf ) was used in the study.…”

Section: Ensemble Classifiermentioning

confidence: 99%

A Novel Approach to Ensemble Classifiers: FsBoost-Based Subspace Method

Noor

Uçar

Polat

et al. 2020

Mathematical Problems in Engineering

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In this article, an algorithm is proposed for creating an ensemble classifier. The name of the algorithm is the F-score subspace method (FsBoost). According to this method, the features are selected with the F-score and classified with different or the same classifiers. In the next step, the ensemble classifier is created. Two versions that are named FsBoost.V1 and FsBoost.V2 have been developed based on classification by the same or different classifiers. According to the results obtained, the results are consistent with the literature. Besides, a higher accuracy rate is obtained compared with many algorithms in the literature. The algorithm is fast because it has a few steps. It is thought that the algorithm will be successful due to these advantages.

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Section: Ensemble Classifiermentioning

confidence: 99%

A Novel Approach to Ensemble Classifiers: FsBoost-Based Subspace Method

Noor

Uçar

Polat

et al. 2020

Mathematical Problems in Engineering

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show abstract

“…The purpose of the SVMs algorithm is to be able to separate the data from each other with the minimum error [51]. For this purpose, it uses the closest data as the support vector machine.…”

Section: ) Support Vector Machinesmentioning

confidence: 99%

Hybrid Artificial Intelligence Based Automatic Determination of Travel Preferences of Chinese Tourists

Topal¹,

Uçar

2019

IEEE Access

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Background and Objective: Tourism and travel sector continues to grow by gaining an important place in the world economy and many countries want to increase their share in this sector. At the same time, it is known that today's consumer tourism and travel purchase decisions are influenced by social media. By examining the data of consumers on social media, it is possible for businesses to reach the right person and get more efficiency from high-cost promotion activities. The study aims to analyze the historical data of users on TripAdvisor with artificial intelligence methods to reveal a profile of consumers who might prefer Turkey. Methods: In this context, TripAdvisor, which is one of the best-known websites in the tourism sector, is an important source of data for countries to increase their share in the tourism market. Inferences can be made by using artificial intelligence methods and the data in TripAdvisor together. In this study, as a case study, the potentials of Chinese tourists to prefer Turkey are dealt because Turkey has increased its tourism targets ten folds for China and the year 2018 was declared as ''Turkey Tourism Year'' in China. In this context, this study aims to determine the potentials of Chinese tourists to prefer Turkey, by processing travel data histories obtained from TripAdvisor with artificial intelligence methods. It is expected that the study will contribute to the tourism sector as well as the academic literature. The study used the travel data history of Chinese tourists taken from TripAdvisor. Significant travel histories were selected by the F-score method. Depending on the selected and all travel histories of users, their travel preferences (Turkey/France) were classified by artificial intelligence algorithms. The developed model was tested with performance criteria. Results: At the end of the study, it was ensured that the Chinese, who would prefer Turkey, were determined with an accuracy rate of 75.25% and sensitivity rate of 0.76. Conclusions: It was observed that it is possible to find the tourists who will prefer Turkey by using the developed system. In other words, the study revealed that the countries can reach the individual instead of masses in their promotional activities.

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“…SVM is one of the best advised ML methods in terms of speed and accuracy [30]. SVM forms optimal hyperplanes in a multidimensional plane and in this way classifies multi-class property data [31].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Breast Cancer Diagnosis by Different Machine Learning Methods Using Blood Analysis Data

Aslan

Çelik

Sabancı

et al. 2018

ijisae

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Today, one of the most common types of cancer is breast cancer. It is crucial to prevent the propagation of malign cells to reduce the rate of cancer induced mortality. Cancer detection must be done as early as possible for this purpose. Machine Learning techniques are used to diagnose or predict the success of treatment in medicine. In this study, four different machine learning algorithms were used to early detection of breast cancer. The aim of this study is to process the results of routine blood analysis with different ML methods and to understand how effective these methods are for detection. Methods used can be listed as Artificial Neural Network (ANN), standard Extreme Learning Machine (ELM), Support Vector Machine (SVM) and K-Nearest Neighbor (k-NN). Dataset used were taken from UCI library. In this dataset age, body mass index (BMI), glucose, insulin, homeostasis model assessment (HOMA), leptin, adiponectin, resistin and chemokine monocyte chemoattractant protein 1 (MCP1) attributes were used. Parameters that have the best accuracy values were found by using four different Machine Learning techniques. For this purpose, hyperparameter optimization method was used. In the end, the results were compared and discussed.

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Scene classification using support vector machines

Cited by 22 publications

References 2 publications

A Novel Approach to Ensemble Classifiers: FsBoost-Based Subspace Method

A Novel Approach to Ensemble Classifiers: FsBoost-Based Subspace Method

Hybrid Artificial Intelligence Based Automatic Determination of Travel Preferences of Chinese Tourists

Breast Cancer Diagnosis by Different Machine Learning Methods Using Blood Analysis Data

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