On efficient agnostic learning of linear combinations of basis functions
We consider efficient agnostic learning of linear combinations of basis functions when the sum of absolute values of the weights of the linear combinations is bounded. With the quadratic loss function, we show that the class of linear combinations of a set of basis functions is efficiently agnostically learnable if and only if the class of basis functions is efficiently agnostically learnable. We also show that the sample complexity for learning the linear combinations grows polynomially if and only if a combinatorial property of the class of basis functions, called the fat-shattering function, grows at most polynomially. We also relate the problem to agnostic learning of f0 1g-valued function classes by showing that if a class of f0 1g-valued functions is efficiently agnostically learnable (using the same function class) with the discrete loss function, then the class of linear combinations of functions from the class is efficiently agnostically learnable with the quadratic loss function.