Abstract
Recently, artificial intelligence methods have been applied in several fields, and their usefulness is attracting attention. These methods are techniques that correspond to models using batch and online processes. Because of advances in computational power, as represented by parallel computing, online techniques with several tuning parameters are widely accepted and demonstrate good results. Neural networks are representative online models for prediction and discrimination. Many online methods require large training data to attain sufficient convergence. Thus, online models may not converge effectively for low and noisy training datasets. For such cases, to realize effective learning convergence in online models, we introduce statistical insights into an existing method to set the initial weights of deep convolutional neural networks. Using an optimal similarity and resampling method, we proposed an initial weight configuration approach for neural networks. For a practice example, identification of biliary atresia (a rare disease), we verified the usefulness of the proposed method by comparing existing methods that also set initial weights of neural networks.
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Hayashi, K., Hoshino, E., Suzuki, M., Nakanishi, E., Sakai, K., Obatake, M. (2023). Detection of the Biliary Atresia Using Deep Convolutional Neural Networks Based on Statistical Learning Weights via Optimal Similarity and Resampling Methods. In: Brito, P., Dias, J.G., Lausen, B., Montanari, A., Nugent, R. (eds) Classification and Data Science in the Digital Age. IFCS 2022. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-031-09034-9_20
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DOI: https://doi.org/10.1007/978-3-031-09034-9_20
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