Statistical classification (or classification) - a
statistical procedure in which individual items are placed into
groups based on quantitative information on one or more
characteristics inherent in the items (referred to as traits,
variables, characters, etc) and based on a training set of
previously labeled items.
One class classifier - classification without
negative samples. The training does not take into account any other,
but the the samples from that particular class. Positive - better
general precision, easy on calculations and convenient for
automation (as training creation and unknown classification);
negative - the visual control is not as intuitive as in multiclass
Spec - spectrum usually
mass-spectrum, but could be well optical or other. Correspond to a
XY ASCII file.
Spec-group (or group) - set of spectra with
something common in between. Usually separate measurements of the
same sample. A group corresponds to one class/cluster in discriminant or cluster analysis.
stand-alone application and add-on to R and it is identical to
spectral data part of Classifion. It
organizes the data, has visualization tools and contains
Module - a functional unit,
user-accessible as module page (bottom half of the workplace) or
thru COM-server interface.
Type I error, or false
negative - misclassification of sample which belong to a class, not
to belong to that class.
Type II error, or false
positive - misclassification of sample which does not belong to a
class, to belong to that class.
(or loadings, or factors) - orthogonal vectors to which the spectral
data have been decomposed.
consecutive excluding of one sample from a set and test that sample
as unknown against the rest of them
Self-prediction - test
a spec from the training set against the whole training set.
Cross-classification - same as self-prediction, but
for groups instead of specs.