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32 Cha pte r O n e
particle swarm optimization Adaptive algorithm based on the social
metaphor of flocking birds (or schooling fish, or swarming insects).
principal component analysis Transforms the input variables to a new set
of variables known as principal components that are orthogonal to each
other. A subset of the principal components that captures most of the
information in the original data is selected for dimensionality reduction.
supervised learning Uses an external teacher to produce a desired output;
the model learns from training examples.
support vector machines Learning kernel-based systems that use a
hypothesis space of linear functions in high-dimensional feature spaces.
unsupervised learning Does not involve an external teacher; the algorithm
discovers collective properties of data by self-organization.
