The bias of an estimator is the difference between its mean and the true value.
bias variance tradeoff
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boosting
bootstrap
An idea for statistical inference, using training sets created by re-sampling with replacement from the original training set, so examples may occur more than once.
Given a fixed number of clusters, we aim to find a grouping of the objects such that similar objects belong to the same cluster
complexity
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.
a translate of the null space of any linear functional; a three-dimensional space in four dimensions, or more generally an (n-1)-space in n dimensions.
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.
learning machine
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learning problem
finding a general rule that explains data given only a sample of limited size
The elements of the output space in preference learning.
regression
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.
regularization
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 instances S is shattered by hypothesis space H if and only if for every dichotomy of S there exists some hypothesis in H consistent with this dichotomy.
Statistical learning theory
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Structural Risk Minimization
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supervised learning
Choosing a classifier from a training set of correctly classified examples.
A set of examples used to tune the parameters of a classifier.
Vapnik
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.