Supervised Learning
Supervised learning, a core machine learning paradigm, aims to train models to predict outputs based on labeled input data. Current research emphasizes improving model efficiency and robustness, particularly in scenarios with limited or noisy data, exploring techniques like self-supervised pre-training, active learning for data selection, and ensemble methods to enhance accuracy and address class imbalances. These advancements are crucial for various applications, from medical image analysis and infrastructure inspection to natural language processing and targeted advertising, enabling more accurate and reliable predictions with less reliance on extensive labeled datasets.
Papers
Supervised Time Series Classification for Anomaly Detection in Subsea Engineering
Ergys Çokaj, Halvor Snersrud Gustad, Andrea Leone, Per Thomas Moe, Lasse Moldestad
DALSA: Domain Adaptation for Supervised Learning From Sparsely Annotated MR Images
Michael Götz, Christian Weber, Franciszek Binczyk, Joanna Polanska, Rafal Tarnawski, Barbara Bobek-Billewicz, Ullrich Köthe, Jens Kleesiek, Bram Stieltjes, Klaus H. Maier-Hein
Combining unsupervised and supervised learning in microscopy enables defect analysis of a full 4H-SiC wafer
Binh Duong Nguyen, Johannes Steiner, Peter Wellmann, Stefan Sandfeld
Solar Panel Segmentation :Self-Supervised Learning Solutions for Imperfect Datasets
Sankarshanaa Sagaram, Krish Didwania, Laven Srivastava, Aditya Kasliwal, Pallavi Kailas, Ujjwal Verma