Greedy function approximation: A gradient boosting machine.

Greedy Function Approximation: A Gradient Boosting Machine

Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive o...

Sparse Greedy Matrix Approximation for Machine Learning

Minimizing a General Penalty Function on a Single Machine via Developing Approximation Algorithms and FPTASs

This paper addresses the Tardy/Lost penalty minimization on a single machine. According to this penalty criterion, if the tardiness of a job exceeds a predefined value, the job will be lost and penalized by a fixed value. Besides its application in real world problems, Tardy/Lost measure is a general form for popular objective functions like weighted tardiness, late work and tardiness with reje...

Predicting the Popularity of Online News using Gradient Boosting Machine

Popularity prediction of online news aims to predict the future popularity of news article prior to its publication estimating the number of shares, likes, and comments. Yet, popularity prediction is a challenging task due to various issues including difficulty to measure the quality of content and relevance of content to users; prediction difficulty of complex online interactions and informati...

Using Machine Learning Gradient Boosting to model commercial activities

As urban population grow, commercial activities play a fundamental role in providing goods, services and are part of cities fabric. Policy makers and urban planners need new tools to study these areas. This paper describes how machine learning can be applied to predict the density of commercial activities in cities. A supervised machine learning method called Gradient Boosting was applied to a ...