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Hybrid Reinforcement Learning Framework for Mixed-Variable Problems
Optimization problems characterized by both discrete and continuous variables are common across various disciplines, presenting unique challenges due to their complex solution landscapes and the difficulty of navigating mixed-variable spaces effectively.
A cost-reducing partial labeling estimator in text classification problem
We propose a new approach to address the text classification problems when learning with partial labels is beneficial.
Centroid estimation based on symmetric KL divergence for Multinomial text classification problem
We define a new method to estimate centroid for text classification based on the symmetric KL-divergence between the distribution of words in training documents and their class centroids.
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