Constructive Approach
Constructive approaches in machine learning focus on building models and algorithms to solve specific problems, often by integrating diverse data sources and leveraging pre-trained models for efficiency. Current research emphasizes the use of deep learning architectures, including convolutional neural networks and transformers, alongside techniques like ensemble learning, transfer learning, and meta-learning, to improve model performance and interpretability across various domains. These approaches are proving valuable in diverse applications, ranging from medical image analysis and fake news detection to robotics and space mission planning, demonstrating the broad impact of constructive methodologies on scientific advancement and practical problem-solving.
Papers
Learning the Approach During the Short-loading Cycle Using Reinforcement Learning
Carl Borngrund, Ulf Bodin, Henrik Andreasson, Fredrik Sandin
Combining Optimal Transport and Embedding-Based Approaches for More Expressiveness in Unsupervised Graph Alignment
Songyang Chen, Yu Liu, Lei Zou, Zexuan Wang, Youfang Lin, Yuxing Chen, Anqun Pan
A Staged Approach using Machine Learning and Uncertainty Quantification to Predict the Risk of Hip Fracture
Anjum Shaik, Kristoffer Larsen, Nancy E. Lane, Chen Zhao, Kuan-Jui Su, Joyce H. Keyak, Qing Tian, Qiuying Sha, Hui Shen, Hong-Wen Deng, Weihua Zhou
Enhancing Plant Disease Detection: A Novel CNN-Based Approach with Tensor Subspace Learning and HOWSVD-MD
Abdelmalik Ouamane, Ammar Chouchane, Yassine Himeur, Abderrazak Debilou, Abbes Amira, Shadi Atalla, Wathiq Mansoor, Hussain Al Ahmad