The computational complexity of learning from binary examples is
investigated for linear threshold neurons. We introduce
combinatorial measures that create classes of infinitely many
learning problems with sample restrictions. We analyze how the
complexity of these problems depends on the values for the measures.
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We study pseudorandom generator (PRG) constructions $G^f : {0,1}^l \to {0,1}^{l+s}$ from one-way functions $f : {0,1}^n \to {0,1}^m$. We consider PRG constructions of the form $G^f(x) = C(f(q_{1}) \ldots f(q_{poly(n)}))$
where $C$ is a polynomial-size constant depth circuit
and $C$ and the $q$'s are generated from $x$ arbitrarily.
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In this paper, we show how to systematically
improve on parameterized algorithms and their
analysis, focusing on search-tree based algorithms
for d-Hitting Set, especially for d=3.
We concentrate on algorithms which are easy to implement,
in contrast with the highly sophisticated algorithms
which have been elsewhere designed to ... more >>>