• Corpus ID: 295023

Overview of machine learning

@inproceedings{Murphy2007OverviewOM,
  title={Overview of machine learning},
  author={Kevin P. Murphy},
  year={2007}
}
The most widely studied problem in machine learning is supervised learning. We are given a labeled training set of input-output pairs, D = (xi, yi)i=1, and have to learn a way to predict the output or target ỹ for a novel test input x̃ (i.e, for x̃ 6∈ D). (We use the tilde notation to denote test cases that we have not seen before.) Some examples include: predicting if someone has cancer ỹ ∈ {0, 1} given some measured variables x̃; predicting the stock price tomorrow ỹ ∈ IR given the stock… 

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References

SHOWING 1-2 OF 2 REFERENCES

The Elements of Statistical Learning

Chapter 11 includes more case studies in other areas, ranging from manufacturing to marketing research, and a detailed comparison with other diagnostic tools, such as logistic regression and tree-based methods.

Kalman filter model of the visual cortex

  • Neural Computation
  • 1997