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Package `greyzoneSurv`
Package `greyzoneSurv`

Using consumer behavior data to reduce energy
Using consumer behavior data to reduce energy

A Prototype-driven Framework for Change Detection in Data Stream Classification,
A Prototype-driven Framework for Change Detection in Data Stream Classification,

... labeled and unlabeled queues. The idea here is to incorporate more examples from impure clusters into the training set. Given a cluster Pi, its impurity is computed as follows: ...
SAMO abstract format
SAMO abstract format



... salient features such as margin maximization and kernel substitution for classification and regression of data in a high dimensional feature space. But SVMs still face difficulties in handling large datasets. This difficulty is because of solving quadratic programming problems in SVMs which is costl ...
large synthetic data sets to compare different data mining methods
large synthetic data sets to compare different data mining methods

... DB-Cart, CHAID or MARS. Each algorithm differs from the others with methods of choosing the next attribute, whose gain is the maximum as a root node [12] and finding optimal question for this parameter. In our case tests are calculated in WEKA, which uses RI algorithm [13]. ...
SELECTION OF SIGNIFICANT VISUAL FEATURES FOR
SELECTION OF SIGNIFICANT VISUAL FEATURES FOR

View Sample PDF - IRMA International
View Sample PDF - IRMA International

Clustering Data Streams: Theory and Practice
Clustering Data Streams: Theory and Practice

Data Mining for Intrusion Detection: from Outliers to True
Data Mining for Intrusion Detection: from Outliers to True

... The request will have the following form: staff.php?FName=John\&LName=Doe \&room=204\&floor=2\&Dpt=RD. This new request, due to the recent recruitment of John Due in this department, should not be considered as an attack. On the other hand, let us consider Ay , an anomaly that corresponds to a true ...
ARAA: A Fast Advanced Reverse Apriori Algorithm for Mining
ARAA: A Fast Advanced Reverse Apriori Algorithm for Mining

... taken by it is 280 which are comparatively higher than the RAA and ARAA. The disadvantages of AA are that it takes number of scans that increase the time complexity of the system very much. The RAA is good for higher data set rule but it is poor for the lower data set rule. RAA also generates first ...
A simple introduction to Markov Chain Monte–Carlo sampling
A simple introduction to Markov Chain Monte–Carlo sampling

... or even 650. Markov chains starting from these values are shown in the middle and right columns of Fig. 1. Examining the top–middle panel of Fig. 1 shows that the Markov chain initially goes quickly down towards the true posterior. After only 80 iterations, the chain is then centered on the true pop ...
Reliability Analysis in the SAS System
Reliability Analysis in the SAS System

... limit on the life axis scale and a lower limit on the stress (temperature) axis scale in the plots. The plots produced by the preceding statements are shown in Figure 5. The plot on the left is an overlaid lognonnal probability plot of the data and the fitted model. The plot on the right is a relati ...
Classification and Analysis of High Dimensional Datasets
Classification and Analysis of High Dimensional Datasets

A Study of the Scaling up Capabilities of Stratified Prototype
A Study of the Scaling up Capabilities of Stratified Prototype

pptx - Neural Network and Machine Learning Laboratory
pptx - Neural Network and Machine Learning Laboratory

Analysis of Dosage-Response Data in Agricultural Research
Analysis of Dosage-Response Data in Agricultural Research

slide
slide

Book Review: Knowledge Discovery in Multiple Databases
Book Review: Knowledge Discovery in Multiple Databases

A Survey on Data Mining Algorithms and Future Perspective
A Survey on Data Mining Algorithms and Future Perspective

Outlier Mining in Large High-Dimensional Data Sets
Outlier Mining in Large High-Dimensional Data Sets

Privacy Preserving Naive Bayes Classifier for Horizontally
Privacy Preserving Naive Bayes Classifier for Horizontally

... The Naive Bayes classifier is a simple but efficient baseline classifier. It is the de facto classifier used for text classification. Naive Bayes is based on a bayesian formulation of the classification problem which uses the simplifying assumption of attribute independence. It is simple to implemen ...
Semi-Lazy Learning: Combining Clustering and Classifiers to Build
Semi-Lazy Learning: Combining Clustering and Classifiers to Build

Advances in Environmental Biology
Advances in Environmental Biology

... rules from databases, on the other hand mining interesting rules is dependent to determining the appropriate threshold that is defined by the user. A great many algorithms and techniques have been designed for discovering ARs from data, such as [6]. These algorithms are Apriori-like, where the AA is ...
A Web-Based Tool for Bayesian and Causal Data Analysis
A Web-Based Tool for Bayesian and Causal Data Analysis

... implementing many of the methods resulting from research by us and others during the years, B-Course has also several unique features not available in any other software we are aware of. In this paper we discuss both the design principles of B-Course, and methods adopted in the implementation of the ...
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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.
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