SAM: A General-purpose Classifier for Modern Predictive Data Analysis

by Tour for R project for statistical computing

The increasing complexity of modern data acquirement poses a great challenge to traditional SVM classifiers in the predictive data analysis. This project aims at providing an efficient and scalable implementation of the Sparse Additive Machine (SAM), which can conduct reliable non-linear classification and variable selection simultaneously. This package has the potential to become a general-purpose classifier for a wide range of data analysis practitioners. It targets at the large-scale classification in the scientific data analysis (e.g. genomics, proteomics, bio-imaging), social media data analysis (e.g. image, audio, video, text modeling) and financial time-series analysis.