What Machine Learning Tells Us About the Mathematical Structure of Concepts
<|reference_start|>What Machine Learning Tells Us About the Mathematical Structure of Concepts: This paper examines the connections among various approaches to understanding concepts in philosophy, cognitive science, and machine learning, with a particular focus on their mathematical nature. By categorizing these approaches into Abstractionism, the Similarity Approach, the Functional Approach, and the Invariance Approach, the study highlights how each framework provides a distinct mathematical perspective for modeling concepts. The synthesis of these approaches bridges philosophical theories and contemporary machine learning models, providing a comprehensive framework for future research. This work emphasizes the importance of interdisciplinary dialogue, aiming to enrich our understanding of the complex relationship between human cognition and artificial intelligence.<|reference_end|>
@article{otsuka2024what,
    title={What Machine Learning Tells Us About the Mathematical Structure of
  Concepts},
    author={Jun Otsuka},
    journal={arXiv preprint arXiv:2408.15507},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.15507},
    primaryClass={cs.AI}
}