1. Aprender
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  3. Support Vector Machines in R
  • 1

    Introduction

    Gratuito

    This chapter introduces some key concepts of support vector machines through a simple 1-dimensional example. Students are also walked through the creation of a linearly separable dataset that is used in the subsequent chapter.

  • 2

    Support Vector Classifiers - Linear Kernels

    Introduces students to the basic concepts of support vector machines by applying the svm algorithm to a dataset that is linearly separable. Key concepts are illustrated through ggplot visualisations that are built from the outputs of the algorithm and the role of the cost parameter is highlighted via a simple example. The chapter closes with a section on how the algorithm deals with multiclass problems.

  • 3

    Polynomial Kernels

    Provides an introduction to polynomial kernels via a dataset that is radially separable (i.e. has a circular decision boundary). After demonstrating the inadequacy of linear kernels for this dataset, students will see how a simple transformation renders the problem linearly separable thus motivating an intuitive discussion of the kernel trick. Students will then apply the polynomial kernel to the dataset and tune the resulting classifier.

  • 4

    Radial Basis Function Kernels

    Builds on the previous three chapters by introducing the highly flexible Radial Basis Function (RBF) kernel. Students will create a "complex" dataset that shows up the limitations of polynomial kernels. Then, following an intuitive motivation for the RBF kernel, students see how it addresses the shortcomings of the other kernels discussed in this course.

Initializing

Exercicio

Visualize the maximal margin separator

In this exercise you will add the maximal margin separator to the scatter plot you created in an earlier exercise. The plot has been reproduced on the right.

Instruções

100 XP
  • Create a data frame called separator containing the maximal margin separator. This is available in the variable mm_separator(enter mm_separator to see it)
  • Use the data frame created to add the maximal margin separator to the sugar content scatterplot created in the earlier exercise and display the result.