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AdaBoost Based Multimodal Learning.

Yu, H. and Cheng, B., 2023. AdaBoost Based Multimodal Learning. In: Benferhat, S., Casini, G., Meyer, T. and Tettamanzi, A., eds. ENIGMA 2023: AI-driven heterogeneous data management: Completing, merging, handling inconsistencies and query-answering. Aachen: CEUR-WS, 16-23.

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This paper focuses on multi-modal learning and introduces an AdaBoost-based approach for multi-modal learning. We address two foundation problems, (1) the difference be-tween AdaBoost with homogeneous and heterogeneous weak learners; (2) generalization metric. By addressing these research questions, this paper enhances our under-standing of AdaBoost in the context of multi-modal learning through comprehensive experiments. The experiment results show that the heterogeneous structure is a trade-off between the performances of different weak learners rather than a clear synergy. The multi-modal learning model's performance depends on how the individual weak learners are composed, and the heterogeneous structure's ad-vantage lies in harnessing the diverse strengths of individual weak learners, even though the improvement achieved is not overwhelmingly pronounced.

Item Type:Book Section
Additional Information:Proceedings of 1st Workshop on AI-driven heterogeneous data management: Completing, merging, handling inconsistencies and query-answering, co-located with 20th International Conference on Principles of Knowledge Representation and Reasoning (KR 2023) Rhodes, Greece, September 3-4, 2023.
Uncontrolled Keywords:AdaBoost; Homogeneous weak learners; Heterogeneous weak learners; Multimodal learning; Generalization metric
ID Code:39200
Deposited By: Symplectic RT2
Deposited On:27 Nov 2023 16:35
Last Modified:27 Nov 2023 16:36


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