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JKS – Seminar Talk. Yanjun Qi 2005.Nov.14. Outline. Mixture of Experts Feature Experts Performance Expression expert – useful or not?. Split our feature sets into four sets  call them as expert. Y. Mixture of Experts. expertP. expertE. expertS. expertF. X. M. Y.

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Jks seminar talk

JKS – Seminar Talk

Yanjun Qi

2005.Nov.14


Outline
Outline

  • Mixture of Experts

  • Feature Experts

  • Performance

  • Expression expert – useful or not?


Mixture of experts

Split our feature sets into four sets

 call them as expert

Y

Mixture of Experts

expertP

expertE

expertS

expertF


Mixture of experts1

X

M

Y

Mixture of Experts

  • Root Gate is input dependent

  • Weight trained by EM

  • Each expert’s parameter also trained by EM



Performance comparison
Performance Comparison

  • logistic regression

  • Naïve bayes

  • Random forest

  • Support vector machine

  • Mixture of 4 feature experts




Expression expert useful or not
Expression expert – useful or not?

  • Due to the last expression expert did not contribute much from its single performance,

  • Found to be most important in RF Gini

  • So we change this expert to the full expert to see how performance changes or delete this expert to see the whole performance change.


Expression expert useful or not1
Expression expert – useful or not?

  •  if we change the last expression expert to the full data, the performance get a little better

  •  if we remove the last expression expert, the performance does not affect much


Delete expert in turn
Delete Expert in turn

  • From the above comparison, we then want to see how performance changes if we delete one of the other three experts




Expression expert useful or not2
Expression expert – useful or not?

  •  Expression experts related features would be useful when combining with others

  •  It is not predictive for the PPI task alone


Acknowledge

Judith Klein-Seetharaman.

Ziv Bar-Joseph

Acknowledge


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