| Name : R-shattering
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| Version : 1.0.4
| Vendor : obs://build_opensuse_org/devel:languages:R
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| Release : lp153.4.13
| Date : 2024-06-14 11:56:31
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| Group : Development/Libraries/Other
| Source RPM : R-shattering-1.0.4-lp153.4.13.src.rpm
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| Size : 0.05 MB
| |
| Packager : (none)
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| Summary : Estimate the Shattering Coefficient for a Particular Dataset
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Description :
The Statistical Learning Theory (SLT) provides the theoretical
background to ensure that a supervised algorithm generalizes the
mapping f:X -> Y given f is selected from its search space bias F. This
formal result depends on the Shattering coefficient function N(F,2n) to
upper bound the empirical risk minimization principle, from which one
can estimate the necessary training sample size to ensure the
probabilistic learning convergence and, most importantly, the
characterization of the capacity of F, including its under and
overfitting abilities while addressing specific target problems. In
this context, we propose a new approach to estimate the maximal number
of hyperplanes required to shatter a given sample, i.e., to separate
every pair of points from one another, based on the recent
contributions by Har-Peled and Jones in the dataset partitioning
scenario, and use such foundation to analytically compute the
Shattering coefficient function for both binary and multi-class
problems. As main contributions, one can use our approach to study the
complexity of the search space bias F, estimate training sample sizes,
and parametrize the number of hyperplanes a learning algorithm needs to
address some supervised task, what is specially appealing to deep
neural networks. Reference: de Mello, R.F. (2019) \"On the Shattering
Coefficient of Supervised Learning Algorithms\" < arXiv:1911.05461> de
Mello, R.F., Ponti, M.A. (2018, ISBN: 978-3319949888) \"Machine
Learning: A Practical Approach on the Statistical Learning Theory\".
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RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
