Posts Tagged ‘machinelearning’

Ensemble Methods in Machine Learning. Proceedings of the First International Workshop on Multiple Classifier Systems

July 13, 2014

Rich C, Alexandru N-M, Geoff C, Alex K (2004) Ensemble selection from libraries of
models. Proceedings of the twenty-first international conference on Machine learning. Banff, Alberta, Canada: ACM.
http://www.niculescu-mizil.org/papers/shotgun.icml04.revised.rev2.pdf

Thomas GD (2000) Ensemble Methods in Machine Learning. Proceedings of the First International Workshop on Multiple Classifier Systems: Springer-Verlag.
http://www.eecs.wsu.edu/~holder/courses/CptS570/fall07/papers/Dietterich00.pdf http://dl.acm.org/citation.cfm?id=743935

.@deniseOme Good ref is TG Dietterich #Ensemble Methods in
#MachineLearning MCS ’00
http://www.eecs.wsu.edu/~holder/courses/CptS570/fall07/papers/Dietterich00.pdf Not rel. to @ensembl #ismb #afp14

ref 17 & 18

Thoughts on “A few useful things to know about machine learning”

February 14, 2013

Some thoughts on a good paper giving intuition on machine learning approaches

http://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf
http://dl.acm.org/citation.cfm?id=2347755

In particular, the paper gives good intuition about:

– overfitting (e.g. how it’s related to multiple testing & bias v variance)
– the curse of dimensionality (in high-D all neighbors look the same)
– the non-practicality of theoretical guarantees
– how different frontiers can give the same prediction
– ensembles (which reduce variance greatly without increasing bias that much)
– ensembles vs Bayesian model averaging (which essentially select the best model)