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Machine Learning: Difference between revisions
→Learning Theory
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Probably Approximately Correct (PAC)<br> | Probably Approximately Correct (PAC)<br> | ||
A hypothesis class <math>H</math> is PAC learnable if given <math>0 < \epsilon, \delta < 1</math>, there is some function <math>m(\epsilon, \delta)</math> polynomial in <math>1/\epsilon, 1/\delta</math> such that if we have a sample size <math>\geq m(\epsilon, \delta)</math> then with probability <math>1-\delta</math> the hypothesis we will learn will have an average error <math>\leq \epsilon</math>. | A hypothesis class <math>H</math> is PAC learnable if given <math>0 < \epsilon, \delta < 1</math>, there is some function <math>m(\epsilon, \delta)</math> polynomial in <math>1/\epsilon, 1/\delta</math> such that if we have a sample size <math>\geq m(\epsilon, \delta)</math> then with probability <math>1-\delta</math> the hypothesis we will learn will have an average error <math>\leq \epsilon</math>. | ||
===Uniform Convergence=== | |||
If for all hypothesis <math>h</math>, <math>|L_S(h)-L_D(h)| \leq \epsilon</math>, then the training set <math>S</math> is called <math>\epsilon</math>-representative. | |||