Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The Teaching Dimension of Kernel Perceptron   Algorithmic machine teaching has been studied under the linear setting where
exact teaching is possible. However, little is known for teaching nonlinear
learners. Here, we establish the sample complexity of teaching, aka teaching
dimension, for kernelized perceptrons for different families of feature maps.
As a warm-up, we show that the teaching complexity is $\Theta(d)$ for the exact
teaching of linear perceptrons in $\mathbb{R}^d$, and $\Theta(d^k)$ for kernel
perceptron with a polynomial kernel of order $k$. Furthermore, under certain
smooth assumptions on the data distribution, we establish a rigorous bound on
the complexity for approximately teaching a Gaussian kernel perceptron. We
provide numerical examples of the optimal (approximate) teaching set under
several canonical settings for linear, polynomial and Gaussian kernel
perceptrons.

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The categories of this paper are: stat
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The categories of this paper are: econ
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The categories of this paper are: nucl-th
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The categories of this paper are: eess
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The categories of this paper are: funct-an
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The categories of this paper are: chem-ph
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The categories of this paper are: physics
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The categories of this paper are: math
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The categories of this paper are: atom-ph
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The categories of this paper are: q-alg
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The categories of this paper are: math-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-ex
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The categories of this paper are: supr-con
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The categories of this paper are: quant-ph