An Investigative Approach to Employ Support Vector Classifier as a Potential Detector of Brain Cancer from MRI Dataset

2021 ◽  
Author(s):  
Mirza Muntasir Nishat ◽  
Fahim Faisal ◽  
Tasnimul Hasan ◽  
Md. Faiyed Bin Karim ◽  
Zahidul Islam ◽  
...  
Keyword(s):  
Brain Cancer ◽  
Support Vector ◽  
Support Vector Classifier ◽  
Investigative Approach

Research on improving accuracy of MWD based on Support vector Classifier and K-proximity method

2021 ◽  
pp. 1-1
Author(s):  
Hai Yang ◽  
Lizao Zhang ◽  
Tao Luo ◽  
Haibo Liang ◽  
Li Li ◽  
...  
Keyword(s):  
Support Vector ◽  
Support Vector Classifier ◽  
Improving Accuracy

A Novel Sparse Weighted Least Squares Support Vector Classifier

2013 ◽  
Vol 842 ◽  
pp. 746-749
Author(s):  
Bo Yang ◽  
Liang Zhang
Keyword(s):  
Least Squares ◽  
Weighted Least Squares ◽  
Importance Measure ◽  
Support Vector ◽  
Comparative Experiment ◽  
Digit Recognition ◽  
Calculating Method ◽  
Imbalance Problem ◽  
Support Vector Classifier

A novel sparse weighted LSSVM classifier is proposed in this paper, which is based on Suykens weighted LSSVM. Unlike Suykens weighted LSSVM, the proposed weighted method is more suitable for classification. The distance between sample and classification border is used as the sample importance measure in our weighted method. Based on this importance measure, a new weight calculating function, using which can adjust the sparseness of weight, is designed. In order to solve the imbalance problem, a kind of normalization weights calculating method is proposed. Finally, the proposed method is used on digit recognition. Comparative experiment results show that the proposed sparse weighted LSSVM can improve the recognition correct rate effectively.

Bayesian Trigonometric Support Vector Classifier

2003 ◽  
Vol 15 (9) ◽  
pp. 2227-2254 ◽  
Author(s):  
Wei Chu ◽  
S. Sathiya Keerthi ◽  
Chong Jin Ong
Keyword(s):  
Loss Function ◽  
Gaussian Processes ◽  
Likelihood Function ◽  
Support Vector ◽  
Data Sets ◽  
Model Adaptation ◽  
Bayesian Techniques ◽  
Benchmark Data ◽  
Support Vector Classifier ◽  
Set Up

This letter describes Bayesian techniques for support vector classification. In particular, we propose a novel differentiable loss function, called the trigonometric loss function, which has the desirable characteristic of natural normalization in the likelihood function, and then follow standard gaussian processes techniques to set up a Bayesian framework. In this framework, Bayesian inference is used to implement model adaptation, while keeping the merits of support vector classifier, such as sparseness and convex programming. This differs from standard gaussian processes for classification. Moreover, we put forward class probability in making predictions. Experimental results on benchmark data sets indicate the usefulness of this approach.

Quaternion support vector classifier

2016 ◽  
Vol 20 (s1) ◽  
pp. S109-S119 ◽  
Author(s):  
G. López-González ◽  
N. Arana-Daniel ◽  
E. Bayro-Corrochano
Keyword(s):  
Support Vector ◽  
Support Vector Classifier

scholarly journals BITCOIN-USD TRADING USING SVM TO DETECT THE CURRENT DAY’S TREND IN THE MARKET

2019 ◽  
Vol 1 (1) ◽  
pp. 70-77
Author(s):  
Ferdiansyah Ferdiansyah ◽  
Edi Surya Negara ◽  
Yeni Widyanti
Keyword(s):  
Exchange Rate ◽  
Foreign Exchange ◽  
Foreign Exchange Market ◽  
Investment Decisions ◽  
Market Value ◽  
Develop Model ◽  
Support Vector ◽  
Prediction Tool ◽  
Exchange Market ◽  
Support Vector Classifier

Cryptocurrency trade is now a popular type of investment. Cryptocurrency market has been treated similar to foreign exchange and stock market. The Characteristics of Bitcoin have made Bitcoin keep rising In the last few years. Bitcoin exchange rate to American Dollar (USD) is $3990 USD on November 2018, with daily pice fluctuations could reach 4.55%2. It is important to able to predict value to ensure profitable investment. However, because of its volatility, there’s a need for a prediction tool for investors to help them consider investment decisions for cryptocurrency trade. Nowadays, computing based tools are commonly used in stock and foreign exchange market predictions. There has been much research about SVM prediction on stocks and foreign exchange as case studies but none on cryptocurrency. Therefore, this research studied method to predict the market value of one of the most used cryptocurrency, Bitcoin. The preditct methods will be used on this research is regime prediction to develop model to predict the close value of Bitcoin and use Support vector classifier algorithm to predict the current day’s trend at the opening of the market

scholarly journals Heart Disease Prediction using Machine Learning

2020 ◽  
Vol 9 (1) ◽  
pp. 700-704
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Disease Prediction ◽  
Nearest Neighbour ◽  
Decision Tree Classifier ◽  
Support Vector Classifier ◽  
Learning Techniques ◽  
Tree Classifier

Deriving the methodologies to detect heart issues at an earlier stage and intimating the patient to improve their health. To resolve this problem, we will use Machine Learning techniques to predict the incidence at an earlier stage. We have a tendency to use sure parameters like age, sex, height, weight, case history, smoking and alcohol consumption and test like pressure ,cholesterol, diabetes, ECG, ECHO for prediction. In machine learning there are many algorithms which will be used to solve this issue. The algorithms include K-Nearest Neighbour, Support vector classifier, decision tree classifier, logistic regression and Random Forest classifier. Using these parameters and algorithms we need to predict whether or not the patient has heart disease or not and recommend the patient to improve his/her health.

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