论文信息 - Fault Diagnosis in Benchmark Process Control System Using Stochastic Gradient Boosted Decision Trees - 字舞流文

Fault Diagnosis in Benchmark Process Control System Using Stochastic Gradient Boosted Decision Trees

Tarun Chopra | Jayashri Vajpai | T. Chopra | Jayashri Vajpai

[1] Larry P. Heck,et al. Trada: tree based ranking function adaptation , 2008, CIKM '08.

[2] Leo Breiman,et al. Classification and Regression Trees , 1984 .

[3] Hongyuan Zha,et al. A regression framework for learning ranking functions using relative relevance judgments , 2007, SIGIR.

[4] Jorma Rissanen,et al. SLIQ: A Fast Scalable Classifier for Data Mining , 1996, EDBT.

[5] Joseba Quevedo,et al. Introduction to the DAMADICS actuator FDI benchmark study , 2006 .

[6] J. Friedman. Stochastic gradient boosting , 2002 .

[7] J. Friedman. Greedy function approximation: A gradient boosting machine. , 2001 .

[8] Casimir A. Kulikowski,et al. Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning and Expert Systems , 1990 .

[9] J. Ross Quinlan,et al. Induction of Decision Trees , 1986, Machine Learning.

[10] Jan Maciej Kościelny,et al. Actuator fault distinguishability study for the DAMADICS benchmark problem , 2006 .