Classification and Machine Learning

Manifold learning (dimensionality reduction)

In machine learning, the data are usually represented in a high dimensional feature space. Nevertheless in practice, the data are restricted to a limited area of the feature space. This leads to the well known problem of the curse of dimensionality. The manifold learning techniques, also known as dimensionality reduction aim to find a mapping of the data from the high dimensional feature space to a new space of lower dimensions. The manifold learning methods estimates the geometry of the dataset locally, around each data point.

Training LS-SVM Classifier in semi-supervised mode

Classification

The learning process typically assumes some form of a priori knowledge of the contextual problem at hand in the form of examplar data associated with labels. These data, called the training set, are used to design a classifier, the performance of which is measured on a separate dataset, called the testing set. This is supervised learning in which the performance of the classifier on the test set is viewed as an estimate of the true performance of the system (i.e. the performance on the whole space).

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