[Adapt] [Seminar] An Introduction to Boosting Methods and XGBoost System

[Kenny Zhu]

Just to remind you: we are meeting in Room 414, not 528 tomorrow.

 

Kenny

 

From: adapt-bounces at cs.sjtu.edu.cn [mailto:adapt-bounces at cs.sjtu.edu.cn] On
Behalf Of Yu Shi
Sent: Wednesday, November 16, 2016 7:48 PM
To: adapt
Subject: [Adapt] [Seminar] An Introduction to Boosting Methods and XGBoost
System

 

Dear Adapters,

     Tomorrow I will give an introduction to boosting and XGBoost. Boosting
is one of the most powerful machine learning ideas introduced in the last
twenty years. Though deep learning is quite popular today, boosting still
occupies most top positions on the leader boards of various data mining
competitions. 

     XGBoost is an open source tree boosting system by Tianqi Chen, a former
ACM class member. It is highly efficient and widely used as a machine
learning tool. XGBoost is also an excellent work which combines algorithm
and system design.

     Let's meet at 1:00 p.m. tomorrow (Thursday) at room 3-528.     

 

Original Paper of XGBoost

https://arxiv.org/abs/1603.02754

XGBoost Website

https://xgboost.readthedocs.io/en/latest/

 

Best,

Yu Shi

------------------

Department of Computer Science and Engineering

Shanghai Jiao Tong University

E-mail: shiyu_k1994 at qq.com

 

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