Transform Regression and the Kolmogorov Superposition Theorem

Pednault, Edwin P. D.

doi:10.1137/1.9781611972764.4

Cited by 17 publications

(17 citation statements)

References 20 publications

(23 reference statements)

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“…Given these data, we can make projections for the future behavior of the same items (Pednault 2004). …”

Section: Results Analysismentioning

confidence: 99%

A knowledge mining framework for business analysts

Palpanas

2012

SIGMIS Database

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Abstract-Several studies have focused on problems related to data mining techniques, including several applications of these techniques in the e-commerce setting. In this work, we describe how data mining technology can be effectively applied in an ecommerce environment, delivering significant benefits to the business analyst. We propose a framework that takes the results of the data mining process as input, and converts these results into actionable knowledge, by enriching them with information that can be readily interpreted by the business analyst. The framework can accommodate various data mining algorithms, and provides a customizable user interface.We experimentally evaluate the proposed approach by using a real-world case study that demonstrates the added benefit of the proposed method. The same study validates the claim that the produced results represent actionable knowledge that can help the business analyst improve the business performance, since it significantly reduces the time needed for data analysis, which results in substantial financial savings.

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“…Given these data, we can make projections for the future behavior of the same items (Pednault 2004). …”

Section: Results Analysismentioning

confidence: 99%

A knowledge mining framework for business analysts

Palpanas

2012

SIGMIS Database

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“…The first paper gives a statistical view of boosting [8] and the second paper introduces transform regression [12]. More detail on these techniques and datasets can be found in the relevant papers.…”

Section: Comparisonmentioning

confidence: 99%

“…In Table 5 the results from [12] on the adult dataset are compared with the results computed by the algorithm presented in this paper. Validation error was again used to select the best generalized additive neural network topology and associated model and to make the results comparable with that in [12].…”

Section: Comparisonmentioning

confidence: 99%

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Generalized Additive Models from a Neural Network Perspective

Waal

Toit

2007

Seventh IEEE International Conference on Data Mining Workshops (ICDMW 2007)

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Recently, an interactive algorithm was proposed for the construction of generalized additive neural networks. Although the proposed method is sound, it has two drawbacks. It is subjective as it relies on the modeler to identify complex trends in partial residual plots and it can be very time consuming as multiple iterations of pruning and adding neurons to hidden layers of the neural network have to be done. In this article, an automatic algorithm is proposed that alleviates both drawbacks. Given a predictive modeling problem, the proposed strategy uses heuristic methods to identify optimal or near optimal generalized additive neural network topologies that are trained to compute the generalized additive model. The neural network approach is conceptually much simpler than many of the other approaches. It is also more accurate as heuristic methods are only used in identifying the appropriate neural network topologies and not in computing the generalized additive models.

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“…The identified patterns represent an approximation of the historical behavior of the items under examination. Given these data, we can make projections for the future behavior [15].…”

Section: Knowledge Mining Frameworkmentioning

confidence: 99%

Knowledge Mining for the Business Analyst

Palpanas

Sairamesh²

Lecture Notes in Computer Science

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Abstract. There is an extensive literature on data mining techniques, including several applications of these techniques in the e-commerce setting. However, all previous approaches require that expert users interpret the data mining results, making them cumbersome to use by business analysts. In this work, we describe a framework that shows how data mining technology can be effectively applied in an e-commerce environment, delivering significant benefits to the business analyst. Using a real-world case study, we demonstrate the added benefit of the proposed method. We also validate the claim that the produced results represent actionable knowledge that can help the business analyst improve the business performance, by significantly reducing the time needed for data analysis, which results in substantial financial savings.

show abstract

Transform Regression and the Kolmogorov Superposition Theorem

Cited by 17 publications

References 20 publications

A knowledge mining framework for business analysts

A knowledge mining framework for business analysts

Generalized Additive Models from a Neural Network Perspective

Knowledge Mining for the Business Analyst

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