vc-formalism
Examples of learning curves from a modified VC-formalism
We examine the issue of evaluation of model specific parameters in a modified VC-formalism. Two examples are analyzed: the 2-dimensional homogeneous perceptron and the I-dimensional higher order neuron. Both models are solved theoretically, and their learning curves are com(cid:173) pared against true learning curves. It is shown that the formalism has the potential to generate a variety of learning curves, including ones displaying ''phase transitions."
MLP Can Provably Generalize Much Better than VC-bounds Indicate
Kowalczyk, Adam, Ferrá, Herman L.
It is also shown that bounds following the true learning curve can be derived from a formalism based on the density of error patterns.
MLP Can Provably Generalize Much Better than VC-bounds Indicate
Kowalczyk, Adam, Ferrá, Herman L.
It is also shown that bounds following the true learning curve can be derived from a formalism based on the density of error patterns.
MLP Can Provably Generalize Much Better than VC-bounds Indicate
Kowalczyk, Adam, Ferrá, Herman L.
It is also shown that bounds following the true learning curve can be derived from a formalism based on the density of error patterns.
Examples of learning curves from a modified VC-formalism
Kowalczyk, Adam, Szymanski, Jacek, Bartlett, Peter L., Williamson, Robert C.
We examine the issue of evaluation of model specific parameters in a modified VC-formalism. Two examples are analyzed: the 2-dimensional homogeneous perceptron and the I-dimensional higher order neuron. Both models are solved theoretically, and their learning curves are compared against true learning curves. It is shown that the formalism has the potential to generate a variety of learning curves, including ones displaying ''phase transitions."
Examples of learning curves from a modified VC-formalism
Kowalczyk, Adam, Szymanski, Jacek, Bartlett, Peter L., Williamson, Robert C.
We examine the issue of evaluation of model specific parameters in a modified VC-formalism. Two examples are analyzed: the 2-dimensional homogeneous perceptron and the I-dimensional higher order neuron. Both models are solved theoretically, and their learning curves are compared against true learning curves. It is shown that the formalism has the potential to generate a variety of learning curves, including ones displaying ''phase transitions."
Examples of learning curves from a modified VC-formalism
Kowalczyk, Adam, Szymanski, Jacek, Bartlett, Peter L., Williamson, Robert C.
We examine the issue of evaluation of model specific parameters in a modified VC-formalism. Two examples are analyzed: the 2-dimensional homogeneous perceptron and the I-dimensional higher order neuron. Both models are solved theoretically, and their learning curves are compared againsttrue learning curves. It is shown that the formalism has the potential to generate a variety of learning curves, including ones displaying ''phase transitions."