Examples of the Use of Data Mining in Financial Applications by Langdell S.

By Langdell S.

This text considers construction mathematical types with monetary information through the use of datamining recommendations. mostly, info mining equipment resembling neural networks and decisiontrees could be a worthy addition to the thoughts to be had to the monetary analyst.However, the knowledge mining innovations are inclined to require extra historic facts than the standardmodels and, relating to neural networks, may be tough to interpret.

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By Langdell S.

This text considers construction mathematical types with monetary information through the use of datamining recommendations. mostly, info mining equipment resembling neural networks and decisiontrees could be a worthy addition to the thoughts to be had to the monetary analyst.However, the knowledge mining innovations are inclined to require extra historic facts than the standardmodels and, relating to neural networks, may be tough to interpret.

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Red and green) [103]. 2. Perceptually Uniform Color Spaces For an image retrieval system, it is important to be able to measure differences between colors in a way that matches perceptual similarity as well as possible. This task is simplified by the use of perceptually uniform color spaces. A color space is perceptually uniform if a small perturbation of a color will produce the same change in perception anywhere in the color space. In 1976, the CIE standardized two perceptually uniform color spaces (since no single system could be agreed upon), Ä£ Ù£ Ú £ (CIELuv) and Ä£ £ £ (CIELab).

Intuitively, the solution is the minimum amount of “work” required to transform one signature into the other. ” In Section 1 we formally define the EMD. We discuss its properties in Section 2 and specify computational issues, including lower bounds, in Section 3. In Section 4 we describe a saturated ground distance and claim better agreement with psychophysics than simpler distance measures. Finally, in Section 5 we mention some extensions to the EMD. 15 The Earth Mover’s Distance 1. DEFINITION The EMD is based on the following linear programming problem: Let ´Ô½ ÛÔ½ µ ´ÔÑ ÛÔÑ µ be the first signature with Ñ clusters, where Ô is the cluster representative and ÛÔ is the weight of the cluster; É ´Õ½ ÛÕ½ µ ´ÕÒ ÛÕÒ µ the second signature with Ò clusters; and the ground distance matrix where ´Ô Õ µ is the ground distance between clusters Ô and Õ .

For example, consider the following two color images: In the first, there are blue skies above a green field, while in the other there is a blue lake below green plants. Although the color distributions might be very similar, the positions of the colors in the image are very different and may have to be taken into account by the query. This can be achieved by modifying the color distance in Section 3 as follows: Instead of using the three-dimensional CIELab color space, we use a five-dimensional space whose first three dimensions are the CIELab color space, and the other two are the ´Ü Ý µ position of each pixel.

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