Options Pricing Via Statistical Learning Techniques: The Support Vector Regression Approach

2008 ◽  
Author(s):  
Panayiotis C. Andreou ◽  
Christakis Charalambous ◽  
Spiros Spiridon Martzoukos
Keyword(s):  
Support Vector Regression ◽  
Statistical Learning ◽  
Options Pricing ◽  
Support Vector ◽  
Learning Techniques ◽  
Regression Approach ◽  
Statistical Learning Techniques

scholarly journals Performance of Classification Techniques along with Support Vector Machine

2019 ◽  
Vol 9 (2) ◽  
pp. 4366-4369
Keyword(s):  
Support Vector Machine ◽  
Statistical Learning ◽  
Predictive Modeling ◽  
Confusion Matrix ◽  
Misclassification Rate ◽  
Support Vector ◽  
Classification Models ◽  
Model Assumption ◽  
Learning Techniques ◽  
Statistical Learning Techniques

Statistical learning is one of the most notable fields studied by the researchers to understand the data in the present scenario. Recent advances in the field of machine learning and artificial intelligence have been keen to develop more powerful automated techniques for predictive modeling, specifically in regression and classification models. These approaches fall under supervised statistical learning techniques, many conventional techniques are very complex to the data when it has larger volumes, i.e., if the data deviates from the model assumption, then the conventional procedure’s results does not have the trustworthy. This paper explores and compares the classical methods with the alternatives in the context of classification, like logistic regression and support vector machine. The efficiency of these procedures has been evaluated through various measures such as confusion matrix and misclassification rate under real environment

scholarly journals Prediction of Rice Cultivation in India—Support Vector Regression Approach with Various Kernels for Non-Linear Patterns

2021 ◽  
Vol 3 (2) ◽  
pp. 182-198
Author(s):  
Kiran Kumar Paidipati ◽  
Christophe Chesneau ◽  
B. M. Nayana ◽  
Kolla Rohith Kumar ◽  
Kalpana Polisetty ◽  
...  
Keyword(s):  
Support Vector Regression ◽  
Rice Yield ◽  
Absolute Error ◽  
Vital Role ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
State Governments ◽  
Learning Techniques ◽  
Regression Approach ◽  
Rice Yields

The prediction of rice yields plays a major role in reducing food security problems in India and also suggests that government agencies manage the over or under situations of production. Advanced machine learning techniques are playing a vital role in the accurate prediction of rice yields in dealing with nonlinear complex situations instead of traditional statistical methods. In the present study, the researchers made an attempt to predict the rice yield through support vector regression (SVR) models with various kernels (linear, polynomial, and radial basis function) for India overall and the top five rice producing states by considering influence parameters, such as the area under cultivation and production, as independent variables for the years 1962–2018. The best-fitted models were chosen based on the cross-validation and hyperparameter optimization of various kernel parameters. The root-mean-square error (RMSE) and mean absolute error (MAE) were calculated for the training and testing datasets. The results revealed that SVR with various kernels fitted to India overall, as well as the major rice producing states, would explore the nonlinear patterns to understand the precise situations of yield prediction. This study will be helpful for farmers as well as the central and state governments for estimating rice yield in advance with optimal resources.

scholarly journals Reply

2005 ◽  
Vol 133 (12) ◽  
pp. 3724-3729 ◽  
Author(s):  
Vladimir M. Krasnopolsky ◽  
Michael S. Fox-Rabinovitz ◽  
Dmitry V. Chalikov
Keyword(s):  
Statistical Learning ◽  
Numerical Models ◽  
Interdisciplinary Field ◽  
Learning Techniques ◽  
Statistical Learning Techniques ◽  
Speed Up ◽  
Network Application ◽  
Similarities And Differences ◽  
Methodological Aspects ◽  
Modern Machine

Abstract This reply is aimed at clarifying and further discussing the methodological aspects of this neural network application for a better understanding of the technique by the journal readership. The similarities and differences of two approaches and their areas of application are discussed. These two approaches outline a new interdisciplinary field based on application of neural networks (and probably other modern machine or statistical learning techniques) to significantly speed up calculations of time-consuming components of atmospheric and oceanic numerical models.

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