This project is targeting people who want to learn internals of ml algorithms or implement them from scratch. The code is much easier to follow than the optimized libraries and easier to play with.

Abstract: Distance metric learning (DML) has received increasing attention in recent years. In this paper, we propose a constrained empirical risk minimization framework for DML. This framework ...

Abstract: Customer retention is a major issue for various service-based organizations particularly telecom industry, wherein predictive models for observing the behavior of customers are one of the ...

Yann LeCun (Courant Institute, NYU) and Corinna Cortes (Google Labs, New York) hold the copyright of MNIST dataset, which is a derivative work from the original NIST datasets. The MNIST database of ...

Support vector regression can predict numeric values effectively, and this article shows how to implement and train a kernel SVR model in C# using stochastic sub-gradient descent.

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BACKGROUND: Hypertension induces structural and functional damage in multiple organs. Evidence of subclinical damage ...

For example, AI applications to medical diagnosis should be regulated very differently from AI applications to self-driving cars. U.S. National Academies report on AI and the Future of Work, study ...

This webpage is a benchmark data set for keystroke dynamics. It is a supplement to the paper "Comparing Anomaly-Detection Algorithms for Keystroke Dynamics," by Kevin Killourhy and Roy Maxion, ...

The Normalised Difference Vegetation Index (NDVI) grids and maps are derived from satellite data. The data provides an overview of the status and dynamics of vegetation across Australia, providing a ...