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].
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].
Note
Proteins
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...