Book Image

Scala for Machine Learning

By : Patrick R. Nicolas
Book Image

Scala for Machine Learning

By: Patrick R. Nicolas

Overview of this book

Table of Contents (20 chapters)
Scala for Machine Learning
About the Author
About the Reviewers

Kernel functions

Every machine learning model introduced in this book so far assumes that observations are represented by a feature vector of a fixed size. However, some real-world applications such as text mining or genomics do not lend themselves to this restriction. The critical element of the process of classification is to define a similarity or distance between two observations. Kernel functions allow developers to compute the similarity between observations without the need to encode them in feature vectors [8:1].

An overview

The concept of kernel methods may be a bit odd at first to a novice. Let's consider the example of the classification of proteins. Proteins have different lengths and compositions, but they do not prevent scientists from classifying them [8:2].



Proteins are polymers of amino acids joined together by peptide bonds. They are composed of a carbon atom bonded to a hydrogen atom, another amino acid, or a carboxyl group.

A protein is represented using a traditional...