The No Free Lunch theorem, discussed below, proves that, in general, the strong sample complexity is infinite, i.e. that there is no algorithm that can learn the globally-optimal target function using a finite number of training samples.
I.e, there is no algorithm that can learn the globally-optimal target function using a finite number of training samples. The No Free Lunch theorem, discussed below, proves that, in general, the strong sample complexity is infinite.
The No Free Lunch theorem, discussed below, proves that, in general, the strong sample complexity is infinite. I.e, there is no algorithm that can learn the globally-optimal target function using a finite number of training samples.
The No Free Lunch theorem, discussed below, proves that, in general, the strong sample complexity is infinite. I.e, there is no algorithm that can learn the globally-optimal target function using a finite number of training samples.