The document discusses combining CART (Classification and Regression Tree) and logistic regression models to take advantage of their respective strengths in classification and data mining tasks. It describes how running a logistic regression on the entire dataset using CART terminal node assignments as dummy variables allows the logistic model to find effects across nodes that CART cannot detect. This improves CART's predictions by imposing slopes on cases within nodes and providing a more granular, continuous response than CART alone. The approach also allows compensating for some of CART's weaknesses like coarse-grained responses.
Big Data Cloud Meetup - Jan 29 2013 - Mike Stonebraker & Scott Jarr of VoltDBBigDataCloud
"Navigating the Database Universe" was the topic of the Big Data Cloud meetup held on Jan 24th 2013 in Santa Clara, CA. This is the presentation made by Mike Stonebraker & Scott Jarr of VoltDB.
This meetup was sponsored by VoltDb.
The Factoring Dead: Preparing for the CryptopocalypseAlex Stamos
A talk at Black Hat USA 2013 discussing recent advances in academic research that imperils two critical algorithms upon which Internet trust rests: RSA and Diffie-Helman.
"Navigating the Database Universe" by Dr. Michael Stonebraker and Scott Jarr,...lisapaglia
Webinar presentation delivered by Dr. Michael Stonebraker and Scott Jarr of VoltDB on December 11, 2012. www.voltdb.com
The design decisions you make today will have a huge performance impact down the line. Until recently, when it came to databases, the choice was easy. Essentially, you had one option: the RDBMS. Today, there's a new universe of databases being thrown into production — and not always with the greatest success. How do you make the right choice for your next application? Database pioneer Dr. Michael Stonebraker and VoltDB co-founder Scott Jarr have some thoughts.
Performance Management in ‘Big Data’ ApplicationsMichael Kopp
Do applications using NoSQL still require performance management? Is it always the best option to throw more hardware at a MapReduce job? In both cases, performance management is still about the application, but "Big Data" technologies have added a new wrinkle.
Big Data Cloud Meetup - Jan 29 2013 - Mike Stonebraker & Scott Jarr of VoltDBBigDataCloud
"Navigating the Database Universe" was the topic of the Big Data Cloud meetup held on Jan 24th 2013 in Santa Clara, CA. This is the presentation made by Mike Stonebraker & Scott Jarr of VoltDB.
This meetup was sponsored by VoltDb.
The Factoring Dead: Preparing for the CryptopocalypseAlex Stamos
A talk at Black Hat USA 2013 discussing recent advances in academic research that imperils two critical algorithms upon which Internet trust rests: RSA and Diffie-Helman.
"Navigating the Database Universe" by Dr. Michael Stonebraker and Scott Jarr,...lisapaglia
Webinar presentation delivered by Dr. Michael Stonebraker and Scott Jarr of VoltDB on December 11, 2012. www.voltdb.com
The design decisions you make today will have a huge performance impact down the line. Until recently, when it came to databases, the choice was easy. Essentially, you had one option: the RDBMS. Today, there's a new universe of databases being thrown into production — and not always with the greatest success. How do you make the right choice for your next application? Database pioneer Dr. Michael Stonebraker and VoltDB co-founder Scott Jarr have some thoughts.
Performance Management in ‘Big Data’ ApplicationsMichael Kopp
Do applications using NoSQL still require performance management? Is it always the best option to throw more hardware at a MapReduce job? In both cases, performance management is still about the application, but "Big Data" technologies have added a new wrinkle.
What Does Big Data Mean and Who Will WinBigDataCloud
Michael Ralph Stonebraker is a computer scientist specializing in database research. He is currently an adjunct professor at MIT, where he has been involved in the development of the Aurora, C-Store, H-Store, Morpheus, and SciDB systems.Through a series of academic prototypes and commercial startups, Stonebraker's research and products are central to many relational database systems on the market today. He is also the founder of a number of database companies, including Ingres, Illustra, Cohera, StreamBase Systems, Vertica, VoltDB, and Paradigm4. He was previously the Chief Technical Officer (CTO) of Informix & a Professor of Computer Science at University of California, Berkeley. He is also an editor for the book "Readings in Database Systems"
Jay Kreps on Project Voldemort Scaling Simple Storage At LinkedInLinkedIn
Jay Kreps on Project Voldemort Scaling Simple Storage At LinkedIn. This was a presentation made at QCon 2009 and is embedded on LinkedIn's blog - http://blog.linkedin.com/
In 10 slides explains bigData. It separates the hype from reality about BigData. Explains what it is and what was already from before. No big numbers, no big claims : just plain simple truth.
The "red pill"
David Loureiro - Presentation at HP's HPC & OSL TESSysFera
David Loureiro, SysFera CEO, talks about "Managing large-scale, heterogeneous infrastructures: from DIET to SysFera-DS" at HP's High Performance Computing and Open Source & Linux Technical Excellence Symposium that took place on the 19-23 March, 2012, in Grenoble, France.
DataStax | Adversarial Modeling: Graph, ML, and Analytics for Identity Fraud ...DataStax
Abstract from paper: Identity theft and the resulting creation of synthetic identities for the purpose of committing fraud, pose a growing challenge to governments and businesses across the globe. This paper describes specific research and conclusions into existing fraud detection data and supporting systems. It describes a novel, ecosystem and process based approach, Adversarial Modeling to combat what must be recognized as a complex, dynamic struggle against organized and efficient adversaries. Adversarial Modeling is a technology and process ecosystem based on distributed computing, graph theory, data mining and machine learning in a focused, purpose-designed Agile derived methodology.
About the Speaker
Rob Murphy
Improve Your Regression with CART and RandomForestsSalford Systems
Why You Should Watch: Learn the fundamentals of tree-based machine learning algorithms and how to easily fine tune and improve your Random Forest regression models.
Abstract: In this webinar we'll introduce you to two tree-based machine learning algorithms, CART® decision trees and RandomForests®. We will discuss the advantages of tree based techniques including their ability to automatically handle variable selection, variable interactions, nonlinear relationships, outliers, and missing values. We'll explore the CART algorithm, bootstrap sampling, and the Random Forest algorithm (all with animations) and compare their predictive performance using a real world dataset.
What Does Big Data Mean and Who Will WinBigDataCloud
Michael Ralph Stonebraker is a computer scientist specializing in database research. He is currently an adjunct professor at MIT, where he has been involved in the development of the Aurora, C-Store, H-Store, Morpheus, and SciDB systems.Through a series of academic prototypes and commercial startups, Stonebraker's research and products are central to many relational database systems on the market today. He is also the founder of a number of database companies, including Ingres, Illustra, Cohera, StreamBase Systems, Vertica, VoltDB, and Paradigm4. He was previously the Chief Technical Officer (CTO) of Informix & a Professor of Computer Science at University of California, Berkeley. He is also an editor for the book "Readings in Database Systems"
Jay Kreps on Project Voldemort Scaling Simple Storage At LinkedInLinkedIn
Jay Kreps on Project Voldemort Scaling Simple Storage At LinkedIn. This was a presentation made at QCon 2009 and is embedded on LinkedIn's blog - http://blog.linkedin.com/
In 10 slides explains bigData. It separates the hype from reality about BigData. Explains what it is and what was already from before. No big numbers, no big claims : just plain simple truth.
The "red pill"
David Loureiro - Presentation at HP's HPC & OSL TESSysFera
David Loureiro, SysFera CEO, talks about "Managing large-scale, heterogeneous infrastructures: from DIET to SysFera-DS" at HP's High Performance Computing and Open Source & Linux Technical Excellence Symposium that took place on the 19-23 March, 2012, in Grenoble, France.
DataStax | Adversarial Modeling: Graph, ML, and Analytics for Identity Fraud ...DataStax
Abstract from paper: Identity theft and the resulting creation of synthetic identities for the purpose of committing fraud, pose a growing challenge to governments and businesses across the globe. This paper describes specific research and conclusions into existing fraud detection data and supporting systems. It describes a novel, ecosystem and process based approach, Adversarial Modeling to combat what must be recognized as a complex, dynamic struggle against organized and efficient adversaries. Adversarial Modeling is a technology and process ecosystem based on distributed computing, graph theory, data mining and machine learning in a focused, purpose-designed Agile derived methodology.
About the Speaker
Rob Murphy
Improve Your Regression with CART and RandomForestsSalford Systems
Why You Should Watch: Learn the fundamentals of tree-based machine learning algorithms and how to easily fine tune and improve your Random Forest regression models.
Abstract: In this webinar we'll introduce you to two tree-based machine learning algorithms, CART® decision trees and RandomForests®. We will discuss the advantages of tree based techniques including their ability to automatically handle variable selection, variable interactions, nonlinear relationships, outliers, and missing values. We'll explore the CART algorithm, bootstrap sampling, and the Random Forest algorithm (all with animations) and compare their predictive performance using a real world dataset.
Using CART For Beginners with A Teclo Example DatasetSalford Systems
Familiarize yourself with CART Decision Tree technology in this beginner's tutorial using a telecommunications example dataset from the 1990s. By the end of this tutorial you should feel comfortable using CART on your own with sample or real-world data.
TreeNet Tree Ensembles & CART Decision Trees: A Winning CombinationSalford Systems
Understand CART decision tree pros/cons, how TreeNet stochastic gradient boosting ca n help overcome single-tree challenges, and what the advantages are when using CART and TreeNet in combination for predictive modeling success.
When building a predictive model in SPM, you'll want to know exactly what you did to get your results. This short slide deck will show you how to review your work in the session logs.