Machine Learning - DISCo

E :
r]:
P:
n:
VE(G):
C:
D :
A bound on the error of a hypothesis (in PAC-learning).
The learning rate in neural network and related learning methods.
The mean of a probability distribution.
The standard deviation of a probability distribution.
The gradient of E with respect to the vector G.
Class of possible target functions.
The training data.
D: A probability distribution over the instance space.
E [x]:
The expected value of x.
E(G): The sum of squared errors of an artifial neural network whose
weights are given by the vector G.
Error: The error in a discrete-valued hypothesis or prediction.
H: Hypothesis space.
h (x):
The prediction produced by hypothesis h for instance x.
P(x): The probability (mass) of x.
Pr(x) : The probability (mass) of the event x.
p(x>: The probability density of x.
Q