Non Parametric

Nonparametric methods in machine learning offer flexible approaches to classification and prediction without making strong assumptions about the underlying data distribution. Current research focuses on improving the efficiency and robustness of nonparametric classifiers, particularly in high-dimensional spaces and with limited data, exploring techniques like nearest neighbor modifications, kernel methods, and online learning algorithms. These advancements address challenges such as the "curse of dimensionality" and the need for efficient computation in big data settings, impacting various fields including image recognition, bioinformatics, and time series analysis. The development of theoretically sound and practically efficient nonparametric methods remains a significant area of ongoing investigation.