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Nov. 15th, 2013
Useful links
Nov. 15th, 2013 03:13 pm"Advances in Data Mining Knowledge Discovery and Applications", open access book:
http://www.intechopen.com/books/advances-in-data-mining-knowledge-discovery-and-applications
Dirichlet Process, Infinite Mixture Models, and Clustering (with program examples). $$
The Dirichlet process provides a very interesting approach to understand group assignments and models for clustering effects. Often time we encounter the k-means approach. However, it is necessary to have a fixed number of clusters. Often we encounter situations where we don’t know how many fixed clusters we need. Suppose we’re trying to identify groups of voters. We could use political partisanship (e.g. low/medium/high Democratic vote) but that may not necessary describe the data appropriately. If this is the case then we can turn to Bayesian nonparametrics and the Dirichlet Process and use some approaches there to solve this problem. Three in particular are commonly used as examples: the Chinese Restaurant Model, Pólya’s Urn, and Stick Breaking.
http://statistical-research.com/dirichlet-process-infinite-mixture-models-and-clustering
Chordalysis: a new method to discover the structure of data
This new method helps you answer "why" - understand the reasons for prediction. It uses chordal graphs to scale the classical method of log-linear analysis to much larger datasets.
http://www.kdnuggets.com/2013/11/chordalysis-new-method-to-discover-structure-data.html
EEG analysis through Machine Learning can help predict Parkinson's
http://blog.neuroelectrics.com/blog/bid/324690/EEG-analysis-through-Machine-Learning-can-help-predict-Parkinson-s
Efficient centralised system for cooperation of multiple robots on complex tasks
http://marblar.com/technology/US8295978
Accelerating Face-in-the-Crowd Recognition with GPU Technology
http://on-demand.gputechconf.com/gtc/2013/webinar/gtc-express-imagus-face-recognition.pdf
STL-11
http://sven-johannsen.de/slides/stl11_201311/stl11.html
http://www.intechopen.com/books/advances-in-data-mining-knowledge-discovery-and-applications
Dirichlet Process, Infinite Mixture Models, and Clustering (with program examples). $$
The Dirichlet process provides a very interesting approach to understand group assignments and models for clustering effects. Often time we encounter the k-means approach. However, it is necessary to have a fixed number of clusters. Often we encounter situations where we don’t know how many fixed clusters we need. Suppose we’re trying to identify groups of voters. We could use political partisanship (e.g. low/medium/high Democratic vote) but that may not necessary describe the data appropriately. If this is the case then we can turn to Bayesian nonparametrics and the Dirichlet Process and use some approaches there to solve this problem. Three in particular are commonly used as examples: the Chinese Restaurant Model, Pólya’s Urn, and Stick Breaking.
http://statistical-research.com/dirichlet-process-infinite-mixture-models-and-clustering
Chordalysis: a new method to discover the structure of data
This new method helps you answer "why" - understand the reasons for prediction. It uses chordal graphs to scale the classical method of log-linear analysis to much larger datasets.
http://www.kdnuggets.com/2013/11/chordalysis-new-method-to-discover-structure-data.html
EEG analysis through Machine Learning can help predict Parkinson's
http://blog.neuroelectrics.com/blog/bid/324690/EEG-analysis-through-Machine-Learning-can-help-predict-Parkinson-s
Efficient centralised system for cooperation of multiple robots on complex tasks
http://marblar.com/technology/US8295978
Accelerating Face-in-the-Crowd Recognition with GPU Technology
http://on-demand.gputechconf.com/gtc/2013/webinar/gtc-express-imagus-face-recognition.pdf
STL-11
http://sven-johannsen.de/slides/stl11_201311/stl11.html