k/n p(x)≈ In practice,we will have to find a compromise for V: o Large enough to include enough examples within R o Small enough to support the assumption that is constant within R Two ways to calculate p(x): o fix Iand determine k from the data,giving rise to the kernel approach,such as histogram,Parzen window ofix k and determine /from the data,which gives rise to the k-nearest-neighbor 8/27p(x) ≃ k/n V ▶ In practice, we will have to find a compromise for V: Large enough to include enough examples within R Small enough to support the assumption that is constant within R ▶ Two ways to calculate p(x): fix V and determine k from the data, giving rise to the kernel approach, such as histogram, Parzen window fix k and determine V from the data, which gives rise to the k-nearest-neighbor 8 / 27