Pekka Orponen

We introduce a model for analog computation with discrete

time in the presence of analog noise

that is flexible enough to cover the most important concrete

cases, such as noisy analog neural nets and networks of spiking neurons.

This model subsumes the classical ...
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Nader Bshouty

We present a new approach to the composition

of learning algorithms (in various models) for

classes of constant VC-dimension into learning algorithms for

more complicated classes.

We prove that if a class $\CC$ is learnable

in time $t$ from a hypothesis class $\HH$ of constant VC-dimension

then the class ...
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Michal Moshkovitz, Dana Moshkovitz

One can learn any hypothesis class $H$ with $O(\log|H|)$ labeled examples. Alas, learning with so few examples requires saving the examples in memory, and this requires $|X|^{O(\log|H|)}$ memory states, where $X$ is the set of all labeled examples. A question that arises is how many labeled examples are needed in ... more >>>