SVM: Support Vector Machines
Process Initialization Dialog
Parameter Information
Sample Selection
The sample selection option indicates whether to cluster genes or experiments.
Process Selection
The SVM algorithm works performing two main processes, training and classification.
One can elect to perform training only, classification only, or both phases of the SVM classification
technique.
The 'Training Only' option results in a set of numerical weights which can stored as a file and used for classification
at a later time.
The 'Classification Only' option takes a file input of weights generated from training and results in a binary classification
of the elements.
The 'Training and Classification' option provides the ability to use the input set as a training set to produce weights which
are immediately applied to perform the classification.
The 'One-out Iterative Validation' iteratively performs an SVM training and classification run.
On each iteration one element is moved to the neutral classification and therefore will not
impact the SVM training nor the classification of elements. The final classification will not
be biased by an initial classification of the element.
** Note that the One-out Iterative Validation will iterate a training and classification
run for each element being classified. This may not be practical nor necessary for the
classification of large numbers of genes. In the case of genes the impact of one gene
in producing a discriminate is relatively small and the time to iteratively run for
each gene could be prohibitive for large sets of genes. This feature is more practical
to use when classifying experiments.
Hierarchical Clustering
This check box selects whether to perform hierarchical clustering on the elements in each cluster
created.