An Adaptive SVR-Based Framework for Multimodal Corpus Classification

Abstract

To address the challenges associated with the dynamic growth and multimodal complexity of modern corpora, an adaptive classification framework based on Support Vector Regression (SVR) was developed. A structured corpus was first constructed, followed by the extraction of salient textual features using Term Frequency–Inverse Document Frequency (TF-IDF) metrics. To accommodate the continuous expansion of the corpus, an incremental learning strategy was employed, enabling the model to update efficiently without complete retraining. A kernel-based SVR model was trained to perform classification tasks, and an adaptive feedback-driven mechanism was introduced to dynamically adjust both model parameters and feature representations based on classification performance metrics. Evaluation was conducted on multiple multilingual and multimodal corpora, with particular emphasis on Chinese language processing, which often presents unique challenges due to character complexity and sparse feature representations. The proposed method achieved a significant improvement in classification accuracy when compared to conventional classification approaches. Furthermore, the model demonstrated superior adaptability and computational efficiency across various corpus types. The findings confirm the viability of SVR as a core component for adaptive classification tasks in dynamic linguistic environments. This study contributes to the field by establishing a generalizable, efficient, and interpretable framework suitable for real-time corpus management systems, intelligent content filtering, and multilingual information retrieval.