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キーワード:
Computer Science, Learning, cs.LG,Statistics, Machine Learning, stat.ML
要旨:
Formal models of learning from teachers need to respect certain criteria to
avoid collusion. The most commonly accepted notion of collusion-freeness was
proposed by Goldman and Mathias (1996), and various teaching models obeying
their criterion have been studied. For each model $M$ and each concept class
$\mathcal{C}$, a parameter $M$-$\mathrm{TD}(\mathcal{C})$ refers to the
teaching dimension of concept class $\mathcal{C}$ in model $M$---defined to be
the number of examples required for teaching a concept, in the worst case over
all concepts in $\mathcal{C}$.
This paper introduces a new model of teaching, called no-clash teaching,
together with the corresponding parameter $\mathrm{NCTD}(\mathcal{C})$.
No-clash teaching is provably optimal in the strong sense that, given any
concept class $\mathcal{C}$ and any model $M$ obeying Goldman and Mathias's
collusion-freeness criterion, one obtains $\mathrm{NCTD}(\mathcal{C})\le
M$-$\mathrm{TD}(\mathcal{C})$. We also study a corresponding notion
$\mathrm{NCTD}^+$ for the case of learning from positive data only, establish
useful bounds on $\mathrm{NCTD}$ and $\mathrm{NCTD}^+$, and discuss relations
of these parameters to the VC-dimension and to sample compression.
In addition to formulating an optimal model of collusion-free teaching, our
main results are on the computational complexity of deciding whether
$\mathrm{NCTD}^+(\mathcal{C})=k$ (or $\mathrm{NCTD}(\mathcal{C})=k$) for given
$\mathcal{C}$ and $k$. We show some such decision problems to be equivalent to
the existence question for certain constrained matchings in bipartite graphs.
Our NP-hardness results for the latter are of independent interest in the study
of constrained graph matchings.
