Sales efforts play a crucial role in dynamic retail management and can have a significant impact on the overall success of a retail business. The primary goal of sales efforts is to generate revenue. By effectively pr...
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Most parameter ID methods use least squares criterion to fit parameter values to observed behavior. However, the least squares criterion can be heavily influenced by outlying data or un-modelled effects. In such cases...
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Most parameter ID methods use least squares criterion to fit parameter values to observed behavior. However, the least squares criterion can be heavily influenced by outlying data or un-modelled effects. In such cases, least squares estimation can yield poor results. Outlying data is often manually removed to avoid inaccurate outcomes, but this process is complex, tedious and operator dependent. This research presents an adaptation of the Levenberg-Marquardt (L-M) parameter identification method that effectively ignores least-square contributions from outlying data. The adapted method (aL-M) is capable of ignoring outlier data in accordance with the coefficient of variation of the residuals and was thus, capable of operator independent omission of outlier data using the 3 standard deviation rule. The aL-M was compared to the original Levenberg-Marquardt (L-M) method in C-peptide, insulin and glucose data. In total three cases were tested: L-M in the full dataset, L-M in the same data where the points that were suspected to be affected by incomplete mixing at the depot site were removed, and the aL-M in the full data set. There were strong correlations between the aL-M and the reduced dataset from [0.85, 0.71] for the clinically valuable glucose parameters. In contrast, the unreduced data yielded poor residuals and poor correlations with the aL-M [0.44, 0.33]. The aL-M approach provided strong justification for consistent removal of data that was deemed to be affected by mixing.
We prove resurgence properties for the Borel transform of a formal power series associated to elements in the Habiro ring that come from radial limits of partial theta series via strange identities. As an application,...
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In combination with the Grenander–Szegö theorem, we observe that a relaxed positivity condition on multipliers, milder than the basic requirement of the Nevanlinna–Odeh multipliers that the sum of the absolute ...
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Magnetohydrodynamic (MHD) waves are routinely observed in the solar atmosphere. These waves are important in the context of solar physics as it is widely believed they can contribute to the energy budget of the solar ...
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A longstanding question in water research is the possibility that supercooled liquid water can undergo a liquid-liquid phase transition (LLT) into high- and low-density liquids. We used several complementary molecular...
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Decision trees are popular machine learning models that are simple to build and easy to interpret. Even though algorithms to learn decision trees date back to almost 50 years, key properties affecting their generaliza...
ISBN:
(纸本)9781713829546
Decision trees are popular machine learning models that are simple to build and easy to interpret. Even though algorithms to learn decision trees date back to almost 50 years, key properties affecting their generalization error are still weakly bounded. Hence, we revisit binary decision trees on real-valued features from the perspective of partitions of the data. We introduce the notion of partitioning function, and we relate it to the growth function and to the VC dimension. Using this new concept, we are able to find the exact VC dimension of decision stumps, which is given by the largest integer d such that 2ℓ ≥ (d⌊d/2⌋), where ≥ is the number of real-valued features. We provide a recursive expression to bound the partitioning functions, resulting in a upper bound on the growth function of any decision tree structure. This allows us to show that the VC dimension of a binary tree structure with N internal nodes is of order N log (N≥). Finally, we elaborate a pruning algorithm based on these results that performs better than the CART algorithm on a number of datasets, with the advantage that no cross-validation is required.
In this paper, some preliminary experimental investigations have been reported for analysing the machining performance characteristics viz. Material Removal Rate (MRR) & Tool Wear Rate (TWR). Electrical Discharge ...
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Cancer is a group of diseases and the second leading cause of death according to World Health Organization. Mathematical and computational methods have been used to explore the cancer cells spread and the mechanism of...
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