Adaptive Hybrid-Kernel Multiple Kernel Learning Support Vector Machine for Text Categorization and Information Retrieval

Text classification and information retrieval remain challenging due to the heterogeneity of textual representations, ranging from sparse lexical features to dense semantic embeddings. This paper presents an adaptive hybrid-kernel multiple kernel learning support vector machine that integrates heterogeneous text representations within a unified and computationally efficient framework. The proposed model combines a linear kernel on term frequency–inverse document frequency word n-grams, a spectrum kernel applied to character n-grams, and a radial basis function kernel applied to sentence embeddings. Kernel contributions are adaptively learned under a simplex constraint with entropy-based regularization to prevent kernel dominance and ensure stable fusion. A focal-hinge loss with class-balanced weighting is incorporated to address class imbalance. Experiments on benchmark text classification and information retrieval datasets demonstrate consistent improvements over strong single-kernel and multiple kernel learning baselines, while kernel approximation techniques maintain scalability with limited performance degradation.