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Weighting quantitative and qualitative variables in clustering methods.

Karina Gibert, Ulises Cortés (1997)

Mathware and Soft Computing

Description of individuals in ill-structured domains produces messy data matrices. In this context, automated classification requires the management of those kind of matrices. One of the features involved in clustering is the evaluation of distances between individuals. Then, a special function to calculate distances between individuals partially simultaneously described by qualitative and quantitative variables is required.In this paper properties and details of the metrics used by Klass in this...

What machines can and cannot do.

Luis M. Laita, Roanes-Lozano, Luis De Ledesma Otamendi (2007)

RACSAM

In this paper, the questions of what machines cannot do and what they can do will be treated by examining the ideas and results of eminent mathematicians. Regarding the question of what machines cannot do, we will rely on the results obtained by the mathematicians Alan Turing and Kurt G¨odel. Turing machines, their purpose of defining an exact definition of computation and the relevance of Church-Turing thesis in the theory of computability will be treated in detail. The undecidability of the “Entscheidungsproblem”...

Wildfires identification: Semantic segmentation using support vector machine classifier

Pecha, Marek, Langford, Zachary, Horák, David, Tran Mills, Richard (2023)

Programs and Algorithms of Numerical Mathematics

This paper deals with wildfire identification in the Alaska regions as a semantic segmentation task using support vector machine classifiers. Instead of colour information represented by means of BGR channels, we proceed with a normalized reflectance over 152 days so that such time series is assigned to each pixel. We compare models associated with 𝓁 1 -loss and 𝓁 2 -loss functions and stopping criteria based on a projected gradient and duality gap in the presented benchmarks.

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