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Large Language Model-Enhanced Algorithm Selection: Towards Comprehensive Algorithm Representation
Following the extraction of embedding vectors for both algorithms and problems, the most suitable algorithm is determined through calculations of matching degrees.
Multi-label Causal Variable Discovery: Learning Common Causal Variables and Label-specific Causal Variables
Analyzing these mechanisms, we provide the theoretical property of common causal variables, based on which the discovery and distinguishing algorithm is designed to identify these two types of variables.
Probabilistic Classification Vector Machine for Multi-Class Classification
The probabilistic classification vector machine (PCVM) synthesizes the advantages of both the support vector machine and the relevant vector machine, delivering a sparse Bayesian solution to classification problems.
Probabilistic Feature Selection and Classification Vector Machine
The proposed method, called probabilistic feature selection and classification vector machine (PFCVMLP ), is able to simultaneously select relevant features and samples for classification tasks.
