Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)  

Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：情報処理学会  

One of the widely used methods in machine learning is the support vector machine (SVM). The SVM is a data classification algorithm based on labeled training data; it is widely used in fields, such as image classification and machine learning. In the SVM, parameter estimation is frequently performed based on mathematical programming methods, such as Sequential Minimal Optimization (SMO), Studies have proposed various algorithms, including interior-point methods. However, although the variables used in an SVM model are generally interrelated, traditional SVMs based on optimization problems cannot consider the relationships between variables in the model, such as the variance-covariance relationship observed in regression analysis. To address this, we attempted to estimate the parameters of an SVM by considering the relationships between the variables through Markov Chain Monte Carlo (MCMC) simulations. Furthermore, through verification experiments using real data, we demonstrated the possibility of estimating the variance and covariance of parameters from sampled data and improving the estimation accuracy through data over-sampling.

Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)  

Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)  

Authorship：Lead author   Language：Japanese   Publishing type：Research paper (scientific journal)  

In stock investment, the financial condition of a company is one of the critical pieces of information for investors. One practical indicator used to understand a company's financial status is credit ratings, which have been utilized for a long time. This research first describes a method for estimating corporate credit ratings from financial indicators using Support Vector Machines (SVM), a widely used classification technique in the field of machine learning. Next, we discuss some issues associated with existing models and propose improvements to the credit rating estimation model using SVM. Furthermore, we aim to demonstrate the superiority of the proposed model through empirical experiments by applying the method to real data.

Event date： 2025.9

Language：Japanese   Presentation type：Oral presentation (general)  

Venue：東京大学弥生キャンパス  

Event date： 2025.9

Presentation type：Public lecture, seminar, tutorial, course, or other speech  

Event date： 2025.9

Language：Japanese   Presentation type：Oral presentation (general)  

Presentation type：Oral presentation (general)  

Venue：オンライン   Country：Japan  

Event date： 2024.11

Presentation type：Oral presentation (general)  

Venue：Fukuoka   Country：Japan  

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