Posts Tagged ‘mining’

The Dark Market for Personal Data –

Sunday, October 26th, 2014

The Dark Market for Personal Data We’re all “judged by a #bigdata Star Chamber of unaccountable decision makers”


We need regulation to help consumers recognize the perils of the new information landscape without being overwhelmed with data. The right to be notified about the use of one’s data and the right to challenge and correct errors is fundamental. Without these protections, we’ll continue to be judged by a big-data Star Chamber of unaccountable decision makers using questionable sources.


Delving into Deep Learning » American Scientist

Thursday, October 16th, 2014

Delving into Deep Learning History of #NeuralNets from perceptrons to today’s complex nets with many hidden layers

My public notes from the Yale Day of Data (#ydod2014, i0dataday)

Tuesday, September 30th, 2014

PLOS Computational Biology: Spatial Generalization in Operant Learning: Lessons from Professional Basketball

Monday, September 29th, 2014

Spatial Generalization in… Learning: Lessons from… #Basketball How past success changes your tendencies to shoot

Describes constructing a learning matrix for how a player will update his tendency to shoot
from a certain region of the court based on his past successes or failures

Biostatistics: iCluster | Memorial Sloan Kettering Cancer Center

Wednesday, September 10th, 2014

simultaneously clustering of cancer data across data types, in a sense related to orthoclust

My public notes from KDD 2014

Sunday, August 31st, 2014 (need password)

Big Data and Its Technical Challenges | July 2014 | Communications of the ACM

Saturday, August 30th, 2014

#BigData & Its Technical Challenges Data acquisition, cleaning, aggregation, analysis, visualization & interpretation

IEEE Xplore Abstract – A Comparative Analysis of Ensemble Classifiers: Case Studies in Genomics

Sunday, August 24th, 2014

Pandey mentions: Comparative Analysis of #Ensemble Classifiers [eg mean agg. or stacking]…in Genomics #kdd2014

performance-diversity tradeoff: should one incl. higher performance, lower diversity ones…. but still adding diversity is good

related to

The Signal and the Noise: Why So Many Predictions Fail — but Some Don’t: Nate Silver: 9781594204111: Amaz Books

Tuesday, July 8th, 2014

Bioengineering and systems biology. Ann Biomed Eng. 2006 – PubMed – NCBI

Saturday, June 14th, 2014

Bioengineering & #systemsbiology. Classic def’n in terms of the “4 M’s”—Measurement, #Mining, Modeling & Manipulation


Systems Biology can also be defined operationally, as
by the MIT Computational & Systems Biology Initiative,
in terms of the “4 M’s”—Measurement, Mining, Modeling,
and Manipulation—illustrated schematically in Fig. 1 (see