This article introduces algorithmic bias in machine learning (ML) based marketing models. Although the dramatic growth of decision making continues to gain momentum marketing, research this stream is still inadequate despite devastating, asymmetric and oppressive impacts on various customer groups. To fill void, study presents a framework identifying sources drawing microfoundations dynamic cap...

In this paper, we show that by combining experimental data from different optical fibers, can build a fiber-agnostic neural-network to model the Raman amplifier. The NN predict gain profile of new fiber type (unseen during training) with maximum absolute error as low 0.22 dB. We generalization is only possible when unseen parameters are similar fibers used model. Therefore, training dataset wid...

Abstract Under what conditions does machine learning (ML) model opacity inhibit the possibility of explaining and understanding phenomena? In this article, I argue that nonepistemic values give shape to ML problem even if we keep researcher interests fixed. Treating models as an instance doing model-based science explain understand phenomena reveals there is (i) external problem, where presence...

In this paper, face detection problem is considered using the concepts of compressive sensing technique. This technique includes dictionary learning procedure and sparse coding method to represent the structural content of input images. In the proposed method, dictionaries are learned in such a way that the trained models have the least degree of coherence to each other. The novelty of the prop...

Machine learning methods used for decision support must achieve (a) high accuracy of decisions they recommend, and (b) deep understanding of decisions, so decision makers could trust them. Methods for learning implicit, non-symbolic knowledge provide better predictive accuracy. Methods for learning explicit, symbolic knowledge produce more comprehensible models. Hybrid machine learning models c...

BACKGROUND Machine learning has a vast range of applications. In particular, advanced machine learning methods are routinely and increasingly used in quantitative structure activity relationship (QSAR) modeling. QSAR data sets often encompass tens of thousands of compounds and the size of proprietary, as well as public data sets, is rapidly growing. Hence, there is a demand for computationally ...