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Bootstrap aggregating ( bagging ) is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regression . It also reduces variance and helps to avoid overfitting . Although it is usually applied to decision tree methods, it can be used with any type of method. Bagging is a special case of the model averaging approach. [ edit ] Description of the technique Given a standard training set D of size n , bagging generates m new training sets
A knowledge market is a mechanism for distributing knowledge resources. There are two views on knowledge and how knowledge markets can function. One view uses a legal construct of intellectual property to make knowledge a typical scarce resource , so the traditional commodity market mechanism can be applied directly to distribute it.
Result of particle filtering (red line) based on observed data generated from the blue line ( Larger version ) In statistics , a particle filter , also known as a sequential Monte Carlo method (SMC) , is a sophisticated model estimation technique based on simulation . [ 1 ] Particle filters are usually used to estimate Bayesian models in which the latent variables are connected in a Markov chain — similar to a hidden Markov model (HMM), but typically where the state space of the latent variables is continuous rather than discrete, and not sufficiently restricted to make exact inference tractable (as, for example, in a linear dynamical system , where the state space of the latent variables is restricted to Gaussian distributions and hence exact inference can be done efficiently using a Kalman filter ).
In statistics , importance sampling is a general technique for estimating properties of a particular distribution , while only having samples generated from a different distribution rather than the distribution of interest. It is related to umbrella sampling in computational physics . Depending on the application, the term may refer to the process of sampling from this alternative distribution, the process of inference, or both. [ edit ] Basic theory Let
In statistics , resampling is any of a variety of methods for doing one of the following: Estimating the precision of sample statistics ( medians , variances , percentiles ) by using subsets of available data ( jackknifing ) or drawing randomly with replacement from a set of data points ( bootstrapping ) Exchanging labels on data points when performing significance tests ( permutation tests , also called exact tests , randomization tests, or re-randomization tests) Validating models by using random subsets (bootstrapping, cross validation ) Common resampling techniques include bootstrapping, jackknifing and permutation tests. [ edit ] Bootstrap