Is SVG a machine learning algorithm?

SVG stands for Scalable Vector Graphic. It can be used to make graphics and animations like in HTML canvas. It is a type of vector graphic that may be scaled up or down. SVG is a web standard for vector-based graphics. It basically defines vector-based graphics in XML format. SVG graphics do NOT lose any quality if they are zoomed or resized.

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Problem with Logistic Regression
What Are SVMs?
Support Vector Machines
1. Introduction
Complete Article List
What is a vector machine learning?
What is machine learning algorithm?
Which of the following is not a machine learning algorithm SVG SVM random forest?
What is the decision tree algorithm?

Why SVG?

SVG images can be created and edited with any text editor.
It can be searched, indexed, scripted, and compressed.
SVG images are scalable.
It can be printed with high quality at any resolution.
Every element and every attribute in SVG files can be animated.

Now let’s understand SVG using an example.

Example: In this example, we will be drawing A SVG Circle in HTML.

HTML

<!DOCTYPE html>
<html>

<body>
  
    <!-- svg tag is used here -->
    <svg width="200" height="200">
        <circle cx="80" cy="80" r="50" 
            stroke="black"
            stroke-width="2" 
            fill="grey" />
    </svg>
</body>

</html>

Output:

Please write comments if you find anything incorrect, or you want to share more information about the topic discussed above

Data Science and Digital Engineering

One of the more prevailing and exciting supervised-learning models with associated learning algorithms that vnalyze data and recognize patterns is support vector machines (SVMs). They are used for solving both regression and classification problems. However, they are mostly used in solving classification problems. SVMs were first introduced by B.E. Boser et al. in 1992 and have become popular because of success in handwritten digit recognition in 1994. Before the emergence of boosting algorithms, for example XGBoost and AdaBoost, SVMs had been commonly used.

If you want to have a consolidated foundation of machine-learning algorithms, you should definitely have it in your arsenal. The algorithm of SVMs is powerful, but the concepts behind are not as complicated as you think.

Problem with Logistic Regression

Logistic regression helps solve classification problems separating the instances into two classes. However, there is an infinite number of decision boundaries, and logistic regression only picks an arbitrary one.

Logistic regression doesn’t care if the instances are close to the decision boundary. Therefore, the decision boundary it picks may not be optimal. If a point is far from the decision boundary, we may be more confident in our predictions. Therefore, the optimal decision boundary should be able to maximize the distance between the decision boundary and all instances (i.e., maximize the margins). That’s why the SVM algorithm is important!.

What Are SVMs?

Given a set of training examples, each marked as belonging to one or the other of two categories, an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a nonprobabilistic binary linear classifier.

The objective of applying SVMs is to find the best line in two dimensions or the best hyperplane in more than two dimensions in order to help us separate our space into classes. The hyperplane (line) is found through the maximum margin (i.e., the maximum distance between data points of both classes).

Support Vector Machines

Imagine a labeled training set is two classes of data points (two dimensions): Alice and Cinderella. To separate the two classes, there are so many possible options of hyperplanes that separate correctly. We can achieve exactly the same result using different hyperplanes. However, if we add new data points, the consequence of using various hyperplanes will be very different in terms of classifying new data point into the right group of class.

Read the full story here.

Ray-I Chang (Department of Engineering Science and Ocean Engineering, National Taiwan University, Taipei, Taiwan), Chung-Yuan Su (Department of Engineering Science and Ocean Engineering, National Taiwan University, Taipei, Taiwan) and Tsung-Han Lin (Department of Engineering Science and Ocean Engineering, National Taiwan University, Taipei, Taiwan)

DOI: 10.4018/IJAMC.2017070103

Abstract

Raster comic would result in bad quality while zooming in/out. Different approaches were proposed to convert comic into vector format to resolve this problem. The authors have proposed methods to vectorize comic contents to provide not only small SVG file size and rendering time, but also better perceptual quality. However, they do not process texture in the comic images. In this paper, the authors improve their previously developed system to recognize texture elements in the comic and use these texture elements to provide better compression and faster rendering time. They propose texture segmentation techniques to partition comic into texture segments and non-texture segments. Then, the element of SVG is applied to represent texture segments. Their method uses CSG (Composite Sub-band Gradient) vector as texture descriptor and uses SVM (Support Vector Machine) to classify texture area in the comic. Then, the ACM (Active Contour Model) combining with CSG vectors is introduced to improve the segmentation accuracy on contour regions. Experiments are conducted using 150 comic images to test the proposed method. Results show that the space savings of our method is over 66% and it can utilize the reusability of SVG syntax to support comic with multiple textures. The average rendering time of the proposed method is over three times faster than the previous methods. It lets vectorized comics have higher performance to be illustrated on modern e-book devices.

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1. Introduction

E-book market grows rapidly in recent years. With the mature development of display technology and popularity of handheld devices, readers’ habit has a significant and steady transition from printed to digital. Converting these comics from printed to digital becomes an important issue. As most of the digital comic data are still in raster formats (such as JPG, BMP, and PNG), they need to face the multiple-resolution problem which results in bad quality when zooming in/out for fitting different display devices. Conventionally, the pixel interpolation method is applied to rescale the image. It causes the degradation of image quality (such as jagged or fuzzy edges). Vector formats, such as SWF and SVG (Scalable Vector Graphics) (http://vectormagic.com/home), the sizes of files they produced are still large. They have tried to limit the file size, but image quality degraded accordingly. In Chang, Yen, and Hsu (2008), Chang and Su (2015), and Su, Chang, and Liu (2011), we applied SVG (the current version is 1.1) in raster-to-vector conversion as it is a graphic standard of W3C. For EPUB 3, SVG can be either inside an XHTML or as a standalone entity with an SVG file extension. Our methods processed comic contents to provide not only small SVG file size and rendering time but also better perceptual quality. Chang et al. adopt Autotrace to vectorize comic images, and then use the vector contour searching algorithms for removing extra spaces, combining the slope of clips, and merging similar color regions to compress the vector images. This method achieves high image quality and compression ratio. In Su et al., we recognize text elements in the comic and use these text elements to provide better compression and novel applications, such as translate comic automatically, text/content-based image search, and storyteller. Then Chang and Su improve our previously system (Chang et al.) to propose the color gradient (CG) vectorization method to identify CG regions for representing the color and the direction of CG in each region. Then, we merge neighboring regions those have the same CG vector as a large CG region and represent it by a single path of SVG with linear gradient syntax. Table 1 shows the difference main features of our previously developed system.

Table 1.

The main features of our previously developed system

Reference	Main Features
(Chang et al., 2007)	• The proposed color clustering and vector contour searching can merge similar color regions and enhance compression ratio and render speed.
(Su et al., 2011)	• It can further reduce the sizes of SVG files. • The OCR results from comic images can be translated into other languages to provide multilingual services. • It can support text/content-based vector image search efficiently. • It can support storyteller functionality.
(Chang & Su, 2015)	• The proposed CGV can identify CG in the comic image and embed it in SVG syntax. • The proposed post-processing can remove redundant points in SVG paths. • The reusability of SVG syntax can further reduce the file size. • It provides not only good perceptual quality but also small file size.

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What is a vector machine learning?

A vector is a tuple of one or more values called scalars. Vectors are built from components, which are ordinary numbers. You can think of a vector as a list of numbers, and vector algebra as operations performed on the numbers in the list.

What is machine learning algorithm?

An ML algorithm, which is a part of AI, uses an assortment of accurate, probabilistic, and upgraded techniques that empower computers to pick up from the past point of reference and perceive hard-to-perceive patterns from massive, noisy, or complex datasets.

Which of the following is not a machine learning algorithm SVG SVM random forest?

Which of the following is not a machine learning algorithm? a) SVGb) SVMc) Random forestd) None of the MentionedExplanation: SVM stands for scalable vector machine. d) None of the MentionedExplanation: Random sampling with replacement is the bootstrap. 6.

What is the decision tree algorithm?

Decision trees use multiple algorithms to decide to split a node into two or more sub-nodes. The creation of sub-nodes increases the homogeneity of resultant sub-nodes. In other words, we can say that the purity of the node increases with respect to the target variable.