Titlebook: Immunohistochemistry in Tumor Diagnostics; Muin S.A. Tuffaha,Hans Guski,Glen Kristiansen Book 2023Latest edition The Editor(s) (if applica

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Muin S. A. Tuffaha,Hans Guski,Glen KristiansenReference for researchers and practitioners in data mining, Many approaches have already been proposed for classification and modeling in the literature. These approaches are usually based on mathematical mod- els. Computer systems can easily handle mathematical models even when they are complicated

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Muin S. A. Tuffaha,Hans Guski,Glen KristiansenReference for researchers and practitioners in data mining, Many approaches have already been proposed for classification and modeling in the literature. These approaches are usually based on mathematical mod- els. Computer systems can easily handle mathematical models even when they are complicated

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平淡而無味

Muin S. A. Tuffaha,Hans Guski,Glen Kristiansenels. Computer systems can easily handle mathematical models even when they are complicated and nonlinear (e.g., neural networks). On the other hand, it is not always easy for human users to intuitively understand mathe- matical models even when they are simple and linear. This is because human infor

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Muin S. A. Tuffaha,Hans Guski,Glen Kristiansenels. Computer systems can easily handle mathematical models even when they are complicated and nonlinear (e.g., neural networks). On the other hand, it is not always easy for human users to intuitively understand mathe- matical models even when they are simple and linear. This is because human infor

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Muin S. A. Tuffaha,Hans Guski,Glen Kristiansenels. Computer systems can easily handle mathematical models even when they are complicated and nonlinear (e.g., neural networks). On the other hand, it is not always easy for human users to intuitively understand mathe- matical models even when they are simple and linear. This is because human infor

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Muin S. A. Tuffaha,Hans Guski,Glen Kristiansenin different occasions (i.e. space, time, factor categories) and they are obtained when prior information play a role in the analysis..Three way data can be analyzed by exploratory methods, i.e., the factorial approach (TUCKER, PARAFAC, CANDECOMP, etc.) as well as confirmatory methods, i.e., the mod

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Muin S. A. Tuffaha,Hans Guski,Glen Kristiansenories and the like becomes a challenging task since many applicative fields generate massive amount of data that are difficult to store and to analyze with traditional techniques [1]. In this paper we propose a strategy for extending standard PCA to such data in the case where the variables values a