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Applied Supervised Learning with R

You're reading from   Applied Supervised Learning with R Use machine learning libraries of R to build models that solve business problems and predict future trends

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Product type Paperback
Published in May 2019
Publisher
ISBN-13 9781838556334
Length 502 pages
Edition 1st Edition
Languages
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Authors (2):
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Jojo Moolayil Jojo Moolayil
Author Profile Icon Jojo Moolayil
Jojo Moolayil
Karthik Ramasubramanian Karthik Ramasubramanian
Author Profile Icon Karthik Ramasubramanian
Karthik Ramasubramanian
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Table of Contents (12) Chapters Close

Applied Supervised Learning with R
Preface
1. R for Advanced Analytics FREE CHAPTER 2. Exploratory Analysis of Data 3. Introduction to Supervised Learning 4. Regression 5. Classification 6. Feature Selection and Dimensionality Reduction 7. Model Improvements 8. Model Deployment 9. Capstone Project - Based on Research Papers Appendix

Features in Scene Dataset


The paper uses the scene dataset for semantic scene classification task. The dataset is a collection of images of natural scenes, where a natural scene may contain multiple objects, such that multiple class labels can describe the scene. For example, a field scene with a mountain in the background. From the paper, we have taken the first figure, which shows two images that are multilabel images depicting two different scenes in a single image. Figure 9.6 is a beach and urban scene, whereas Figure 9.7 shows mountains:

Figure 9.6: A beach and urban scene.

Figure 9.7: A mountains scene.

From the given images, we could use the following:

  • Color information: This information is useful when differentiating between certain types of outdoor scenes.

  • Spatial information: This information is useful in various cases. For example, light, warm colors at the top of the image may correspond to sunrise.

The paper uses CIE L*U*V*, such as space, denoted as Luv. Luv space proposes the anticipated...

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