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bigdata stream mining tutorial
bigdata stream mining tutorial

On Data Mining and Classification Using a Bayesian
On Data Mining and Classification Using a Bayesian

preprint  - biomed.cas.cz
preprint - biomed.cas.cz

Using Gaussian Measures for Efficient Constraint Based
Using Gaussian Measures for Efficient Constraint Based

A Combination Approach to Web User Profiling
A Combination Approach to Web User Profiling

Visualizing interestingness
Visualizing interestingness

Incremental Response Modeling Using SAS® Enterprise Miner™
Incremental Response Modeling Using SAS® Enterprise Miner™

labeler - Interactive Computing Lab
labeler - Interactive Computing Lab

A Practical Approach to Classify Evolving Data Streams: Training with Limited Amount of Labeled Data
A Practical Approach to Classify Evolving Data Streams: Training with Limited Amount of Labeled Data

The BAY-HIST Prediction Model for RDF Documents
The BAY-HIST Prediction Model for RDF Documents

Expanding an abridged life table
Expanding an abridged life table

... aspects of splines at a greater length. Applications are concerned with the construction of some U.K. National Life Tables. Details of that may also be found in the work of McCutcheon and Eilbeck (1977). An interesting survey article on splines in Statistics is the one of Wegman, and Wright (1983), ...
SINGLE-SPECIES, SINGLE-SEASON OCCUPANCY MODELS
SINGLE-SPECIES, SINGLE-SEASON OCCUPANCY MODELS

... butterfly species. You select 250 study sites, and set out to survey each site three times in quick succession (or quick enough to assume that the site does not change in occupancy status between surveys). Or, alternatively, if the site is large enough, the site could have been sampled in three diff ...
Clustering Time Series Data An Evolutionary
Clustering Time Series Data An Evolutionary

... 產生最適個體 ...
Comparison of K-means, Normal Mixtures and Probabilistic-D Clustering for B2B Segmentation using Customers’ Perceptions
Comparison of K-means, Normal Mixtures and Probabilistic-D Clustering for B2B Segmentation using Customers’ Perceptions

Conditional Anomaly Detection - UF CISE
Conditional Anomaly Detection - UF CISE

... new and intriguing classes of anomalies, it is perhaps more important to ensure that those data points that a method does find are in fact surprising. To accomplish this, we ask the questions: What is the biggest source of inaccuracy for existing anomaly detection methods? Why might they return a la ...
Distance-based and Density-based Algorithm for Outlier Detection
Distance-based and Density-based Algorithm for Outlier Detection

Delta Boosting Machine and its Application in Actuarial Modeling
Delta Boosting Machine and its Application in Actuarial Modeling

Trajectory Boundary Modeling of Time Series for Anomaly Detection
Trajectory Boundary Modeling of Time Series for Anomaly Detection

... Our goal is to produce an anomaly detection system whose model is transparent. In addition, testing must be online, fast, and generalize when given more than one training series. By online, we mean that each test point receives an anomaly score, with an upper bound on computation time. We accept tha ...
draft pdf
draft pdf

An Overview of Some Recent Developments in Bayesian Problem
An Overview of Some Recent Developments in Bayesian Problem

... information is specified in the form of conditional probability tables. For each node the table specifies the probability of each possible state of the node given each possible combination of states of its parents. The tables for root nodes just contain unconditional probabilities. The key feature o ...
Music Similarity Estimation with the Mean
Music Similarity Estimation with the Mean

... of W can be interpreted as templates for representing input vectors. As only the means depend on hm , this model is called mRBM (Fig. 2a). It is the standard type of RBM to model realvalued inputs. However, independent Gaussian noise does not yield a good generative model for most real-world data. T ...
Towards Robust Conformance Checking
Towards Robust Conformance Checking

... Many existing conformance checking techniques require process models in the form of Petri nets (e.g. [2,7,11]). Given a Petri net and an event log, various conformance metrics are calculated by replaying the log in the net. However, there are at least two drawbacks of Petri net-based conformance che ...
The Learning Intelligent Distribution Agent (LIDA)
The Learning Intelligent Distribution Agent (LIDA)

Beating Kaggle the easy way - Knowledge Engineering Group
Beating Kaggle the easy way - Knowledge Engineering Group

Sample
Sample

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Mixture model

In statistics, a mixture model is a probabilistic model for representing the presence of subpopulations within an overall population, without requiring that an observed data set should identify the sub-population to which an individual observation belongs. Formally a mixture model corresponds to the mixture distribution that represents the probability distribution of observations in the overall population. However, while problems associated with ""mixture distributions"" relate to deriving the properties of the overall population from those of the sub-populations, ""mixture models"" are used to make statistical inferences about the properties of the sub-populations given only observations on the pooled population, without sub-population identity information.Some ways of implementing mixture models involve steps that attribute postulated sub-population-identities to individual observations (or weights towards such sub-populations), in which case these can be regarded as types of unsupervised learning or clustering procedures. However not all inference procedures involve such steps.Mixture models should not be confused with models for compositional data, i.e., data whose components are constrained to sum to a constant value (1, 100%, etc.). However, compositional models can be thought of as mixture models, where members of the population are sampled at random. Conversely, mixture models can be thought of as compositional models, where the total size of the population has been normalized to 1.
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