Tiny Classifier

Tiny classifiers are compact machine learning models designed for resource-constrained environments, aiming to achieve high classification accuracy with minimal computational resources and power consumption. Research focuses on developing efficient algorithms, such as probabilistic classifiers and evolutionary methods for generating optimized logic circuits, and adapting existing architectures like sparse coding for limited data scenarios. These advancements are significant for deploying machine learning on edge devices like IoT sensors and mobile phones, enabling real-time applications in diverse fields including healthcare and bioinformatics, where data scarcity and power limitations are common challenges.