forecasting knowledge extraction by computational intelligence
forecasting knowledge extraction by computational intelligence

Diapositiva 1 - Taiwan Evolutionary Intelligence Laboratory
Diapositiva 1 - Taiwan Evolutionary Intelligence Laboratory

Robust Outlier Detection Technique in Data Mining- A
Robust Outlier Detection Technique in Data Mining- A

Mining Quantitative Association Rules on Overlapped Intervals
Mining Quantitative Association Rules on Overlapped Intervals

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

An Algorithm for Discovering Clusters of Different Densities or
An Algorithm for Discovering Clusters of Different Densities or

A Three-Scan Mining Algorithm for High On
A Three-Scan Mining Algorithm for High On

Neural Computing Research Group Aston University, Birmingham, UK Technical Report: NCRG/96/001 Available from:
Neural Computing Research Group Aston University, Birmingham, UK Technical Report: NCRG/96/001 Available from:

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

An Efficient Density-based Approach for Data Mining Tasks
An Efficient Density-based Approach for Data Mining Tasks

IOSR Journal of Computer Engineering (IOSR-JCE)
IOSR Journal of Computer Engineering (IOSR-JCE)

... clustering and sorting of objects among their many activities, we expect that a study of ant colonies can provide new insights for clustering techniques. Data may be clustered using an iterative version of the Fuzzy C means (FCM) algorithm, but the drawback of FCM algorithm is that it is very sensit ...
Mining Sequential Patterns of Event Streams in a Smart Home Application
Mining Sequential Patterns of Event Streams in a Smart Home Application

Empirical Likelihood Confidence Region for Parameters in Semi
Empirical Likelihood Confidence Region for Parameters in Semi

Bayesian optimization - Research Group Machine Learning for
Bayesian optimization - Research Group Machine Learning for

Fast and Provably Good Seedings for k
Fast and Provably Good Seedings for k

The Simulated Greedy Algorithm for Several Submodular Matroid Secretary Problems Princeton University
The Simulated Greedy Algorithm for Several Submodular Matroid Secretary Problems Princeton University

... Online Accept or reject an element immediately and irrevocably ...
logistic
logistic

A Privacy Preserving Algorithm that Maintains Association Rules
A Privacy Preserving Algorithm that Maintains Association Rules

Predicting Future Decision Trees from Evolving Data
Predicting Future Decision Trees from Evolving Data

Cluster Analysis of Economic Data
Cluster Analysis of Economic Data

Prior approval: The growth of Bayesian methods in psychology
Prior approval: The growth of Bayesian methods in psychology

Test
Test

CS440 - Assignment 3
CS440 - Assignment 3

... Attribute rating shows how popular a product is and its values are between 1-5. The following statistics are available about the relations. Product Company T(Product) = 45000 T(Company)= 500 V(Product, company-name)=300 V(Product, rating)=5 The following query returns the products with rating of 5 t ...
A hybrid knowledge discovery system for oil spillage risks pattern
A hybrid knowledge discovery system for oil spillage risks pattern

... Artificial Intelligence Research ...
Dynamic Programming for Partially Observable Stochastic Games
Dynamic Programming for Partially Observable Stochastic Games

Expectation–maximization algorithm



In statistics, an expectation–maximization (EM) algorithm is an iterative method for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.