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A new variant of online binary passive-aggressive algorithm for interval data | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقالات آماده انتشار، اصلاح شده برای چاپ، انتشار آنلاین از تاریخ 08 اسفند 1404 اصل مقاله (6.28 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2025.35682.5305 | ||
| نویسندگان | ||
| Marziyeh Talebi1؛ Mojtaba Baymani* 1؛ Nima Salehi-M.2 | ||
| 1Department of Mathematics, Faculty of Engineering Science, Quchan University of Technology, Quchan, Iran | ||
| 2Department of Computer Engineering, Faculty of Electrical and Computer Engineering, Quchan University of Technology, Quchan, Iran | ||
| تاریخ دریافت: 29 مهر 1403، تاریخ بازنگری: 23 دی 1403، تاریخ پذیرش: 30 دی 1403 | ||
| چکیده | ||
| Online algorithms process data sequentially, updating their parameters as new data points arrive to minimize prediction errors. However, traditional online algorithms are not inherently designed to handle interval data, which poses a significant challenge in many real-world applications. In this paper, we propose a novel online binary classification algorithm specifically tailored for interval data. We introduce a new loss function that extends the online passive-aggressive (PA) framework, enabling it to effectively handle both ordinal and interval data. The proposed loss function is computationally efficient and provides a robust solution for online learning in the presence of interval data. We provide theoretical guarantees for the algorithm, including cumulative squared loss bounds and relative loss bounds, which demonstrate its effectiveness. Empirical evaluations on multiple datasets show that our method achieves competitive performance compared to existing online algorithms while uniquely addressing the challenges posed by interval data. | ||
| کلیدواژهها | ||
| Online Learning؛ Binary Classification؛ Interval Data؛ Optimization Problem؛ Loss Function؛ Passive-Aggressive Algorithms | ||
| مراجع | ||
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