About this document
Classifier Adaptability for Parallelism by BaidyaNathSaha is a document available to read on EtoBox.
The paper introduces CAMP (Classifier Adaptability Mapping for Parallelism), a framework for categorizing machine learning classifiers based on their adaptability to various parallel execution paradigms, including MapReduce, GPU optimization, and CPU threading models. It provides a taxonomy that helps practitioners select appropriate models and frameworks for scalable performance across different architectures while identifying bottlenecks and proposing strategies to overcome them. The study evaluates the e
- Author
- BaidyaNathSaha
- Language
- EN