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linearity and a linear output layer. The most popular choice for the non-linearity is the
Gaussian. RBF networks have the advantage of not being locked into local minima as
do the feed-forward networks such as the multilayer perceptron.
Support Vector Machines
Support vector machines (SVM) are used to detect and exploit complex patterns in data
by clustering, classifying and ranking the data. They are learning machines that are
used to perform binary classifications and regression estimations. They commonly use
kernel based methods to apply linear classification techniques to non-linear
classification problems. There are a number of types of SVM such as linear, polynomial,
sigmoid etc.
Naïve Bayes
Naïve Bayes based on Bayes conditional probability rule is used for performing
classification tasks. Naïve Bayes assumes the predictors are statistically independent
which makes it an effective classification tool that is easy to interpret. It is best
employed when faced with the "curse of dimensionality" problem, i.e. when the number
of predictors is very high.
k-Nearest Neighbors
The nearest neighbor algorithm (KNN) belongs to the class of pattern recognition
statistical methods. The method does not impose a priori any assumptions about the
distribution from which the modeling sample is drawn. It involves a training set with both
positive and negative values. A new sample is classified by calculating the distance to
the nearest neighboring training case. The sign of that point will determine the
classification of the sample. In the k-nearest neighbor classifier, the k nearest points are
considered and the sign of the majority is used to classify the sample. The performance
of the k-NN algorithm is influenced by three main factors: (1) the distance measure used
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