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Visualizing High Dimensional Data

Visualizing High Dimensional Data. Tuesday, June 13, 2017. On Strip Overfitting of Multidimensional Regression. Overfitting is a key characteristic for regression style data modeling with deep neural networks. Whereas considerable effort have been devoted to finding methods to reduce overfitting, little have been done to address the question how to measure overfitting. In most cases, overfitting is measured by prediction error. Strip Overfitting with respect to point A and B. 3), (B', 4); and we define i...

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Visualizing High Dimensional Data | jamesxli.blogspot.com Reviews

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Visualizing High Dimensional Data. Tuesday, June 13, 2017. On Strip Overfitting of Multidimensional Regression. Overfitting is a key characteristic for regression style data modeling with deep neural networks. Whereas considerable effort have been devoted to finding methods to reduce overfitting, little have been done to address the question how to measure overfitting. In most cases, overfitting is measured by prediction error. Strip Overfitting with respect to point A and B. 3), (B', 4); and we define i...

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Visualizing High Dimensional Data | jamesxli.blogspot.com Reviews

https://jamesxli.blogspot.com

Visualizing High Dimensional Data. Tuesday, June 13, 2017. On Strip Overfitting of Multidimensional Regression. Overfitting is a key characteristic for regression style data modeling with deep neural networks. Whereas considerable effort have been devoted to finding methods to reduce overfitting, little have been done to address the question how to measure overfitting. In most cases, overfitting is measured by prediction error. Strip Overfitting with respect to point A and B. 3), (B', 4); and we define i...

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Visualizing High Dimensional Data: GMS with multiple scanning and aggregated affinitity

http://jamesxli.blogspot.com/2015/05/gms-with-multiple-scanning-and.html

Visualizing High Dimensional Data. Tuesday, May 12, 2015. GMS with multiple scanning and aggregated affinitity. As said in the title, this note is going to put forward two variations to the GMS model. Both variations aim to create better visualization for discrete sequences. For the first variation, we have seen in a previous note that the loopy GMS. That identifies the different clone sequences is added to the scanning vectors. This index p. Will be used like the time-stamp to reduce the affinities betw...

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Visualizing High Dimensional Data: December 2012

http://jamesxli.blogspot.com/2012_12_01_archive.html

Visualizing High Dimensional Data. Wednesday, December 19, 2012. New VisuMap release with support for k-NN. The following short video shows a simple scenario where k-NN is used to match the sphere surface with that of torus:. Posted by James Li. Subscribe to: Posts (Atom). As a software company, we provide software and services to help people to explore and understand high dimensional complex data. View my complete profile. New VisuMap release with support for k-NN.

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Visualizing High Dimensional Data: September 2012

http://jamesxli.blogspot.com/2012_09_01_archive.html

Visualizing High Dimensional Data. Monday, September 17, 2012. Data Visualization on Dual Monitor Systems. I have just uploaded a short video to youtube that demonstrates the capability of VisuMap. To visualize large dataset on a dual monitor system. It is the first time that I used a HD. Carmcorder to make videos for YouTube. It was a challenge to squeeze the large screen HD pixels to youtube sized videos, I hope the video shows at least some interactive perspectives of the software product.

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Visualizing High Dimensional Data: GMS for DNA profiling

http://jamesxli.blogspot.com/2015/05/gms-for-dna-profiling.html

Visualizing High Dimensional Data. Monday, May 25, 2015. GMS for DNA profiling. In this note I am going to describe a GMS based algorithm to convert DNA sequences to geometrical shapes with visually identifiable features. I'll apply this algorithm to real genetic sequences to demonstrate its profiling capability. The main steps of the DNA profiling algorithm. Are illustrated as follows:. Which will produce a set of high dimensional vectors. As described in a previous node. And the affinity decay speed λ.

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Visualizing High Dimensional Data: October 2012

http://jamesxli.blogspot.com/2012_10_01_archive.html

Visualizing High Dimensional Data. Saturday, October 6, 2012. Generalized Relational Perspective Map. This post describes a variation of the algorithm Relational Perspective Map. As a MDS ( multidimensional scaling. Algorithm, RPM seeks to map a set of data points from high dimensional space to the surface of a torus, so that the following "Energy" is minimal:. Is the distance matrix between the data points in the high dimensional space; and d. On each other; and the closer two "particles" are on the tor...

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Visualizing High Dimensional Data

Visualizing High Dimensional Data. Tuesday, June 13, 2017. On Strip Overfitting of Multidimensional Regression. Overfitting is a key characteristic for regression style data modeling with deep neural networks. Whereas considerable effort have been devoted to finding methods to reduce overfitting, little have been done to address the question how to measure overfitting. In most cases, overfitting is measured by prediction error. Strip Overfitting with respect to point A and B. 3), (B', 4); and we define i...

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