Given a fixed number of clusters, we aim to find a grouping of the objects such that similar objects belong to the same cluster
any of various measures of the difficulty of a given decision problem, computational method, or algorithm; for example, the total number of bits, flops, or operations used may be regarded as approximately a function of the size of the problem, or the amount of work involved in its solution.
the fundamental statistical result that the average of a sequence of n independent identically distributed random variables tends to their common mean as n tends to infinity, whence the relative frequency of the occurrence of an event in n independent repetitions of an experiment tends to its probability as n increases without limit.
finding a general rule that explains data given only a sample of limited size
The elements of the output space in preference learning.
the analysis or measure of the association between a dependent variable and one or more independent variables, usually formulated as an equation in which the independent variables have parametric coefficients, which may enable future values of the dependent variable to be predicted.
A class of methods of avoiding over-fitting to the training set by penalizing the fit by a measure of 'smoothness' of the fitted function.
A set of examples used to tune the parameters of a classifier.
VC dimension (Vapnik-Chervonenkis dimension)
The VC dimension, VC(H), of hypothesis space H defined over instance space X is the size of the largest finite subset of X shattered by H. If arbitrarily large finite sets of X can be shattered by H, then VC(H) is identically equal to infinity.