Blog Archives

Deep Learning in R

August 14, 2016
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Deep Learning in R

Deep learning has a wide range of applications, from speech recognition, computer vision, to self-driving cars and mastering the game of Go. While the concept is intuitive, the implementation is often tedious and heuristic. We will take a stab at sim...

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Deep Learning in R

August 14, 2016
By
Deep Learning in R

Deep learning has a wide range of applications, from speech recognition, computer vision, to self-driving cars and mastering the game of Go. While the concept is intuitive, the implementation is often tedious and heuristic. We will take a stab at sim...

Read more »

Hierarchical Linear Model

July 22, 2013
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Hierarchical Linear Model

Linear regression probably is the most familiar technique in data analysis, but its application is often hamstrung by model assumptions. For instance, if the data has a hierarchical structure, quite often the assumptions of linear regression are feas...

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Hierarchical Linear Model

July 22, 2013
By
Hierarchical Linear Model

Linear regression probably is the most familiar technique in data analysis, but its application is often hamstrung by model assumptions. For instance, if the data has a hierarchical structure, quite often the assumptions of linear regression are feas...

Read more »

Bayesian Classification with Gaussian Process

January 6, 2013
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Bayesian Classification with Gaussian Process

Despite prowess of the support vector machine, it is not specifically designed to extract features relevant to the prediction. For example, in network intrusion detection, we need to learn relevant network statistics for the network defense. In consu...

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Bayesian Classification with Gaussian Process

January 6, 2013
By
Bayesian Classification with Gaussian Process

Despite prowess of the support vector machine, it is not specifically designed to extract features relevant to the prediction. For example, in network intrusion detection, we need to learn relevant network statistics for the network defense. In consu...

Read more »

Bayesian Inference Using OpenBUGS

July 23, 2012
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Bayesian Inference Using OpenBUGS

In our previous statistics tutorials, we have treated population parameters as fixed values, and provided point estimates and confidence intervals for them. An alternative approach is the Bayesian statistics. It treats population parameters as random...

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Bayesian Inference Using OpenBUGS

July 23, 2012
By
Bayesian Inference Using OpenBUGS

In our previous statistics tutorials, we have treated population parameters as fixed values, and provided point estimates and confidence intervals for them. An alternative approach is the Bayesian statistics. It treats population parameters as random...

Read more »

Significance Test for Kendall’s Tau-b

April 15, 2012
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Significance Test for Kendall’s Tau-b

A variation of the standard definition of Kendall correlation coefficient is necessary in order to deal with data samples with tied ranks. It known as the Kendall’s tau-b coefficient and is more effective in determining whether two non-parametr...

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Support Vector Machine with GPU, Part II

October 22, 2011
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Support Vector Machine with GPU, Part II

In our last tutorial on SVM training with GPU, we mentioned a necessary step to pre-scale the data with rpusvm-scale, and to reverse scaling the prediction outcome. This cumbersome procedure is now simplified with the latest RPUSVM. read more

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