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Welcome to the Open Relevance Project!
"The Open Relevance Project (ORP) is a new Apache Lucene sub-project aimed at making materials for doing relevance testing for Information Retrieval (IR), Machine Learning and Natural Language Processing (NLP) into open source.
Our initial focus is on creating collections, judgments, queries and tools for the Lucene ecosystem of projects (Lucene Java, Solr, Nutch, Mahout, etc.) that can be used to judge relevance in a free, repeatable manner. "
Elements of Statistical Learning: data mining, inference, and prediction. 2nd Edition.
Wow! PDF file of the whole book online, plus some R examples.
UCI Machine Learning Repository: Netflix Prize Data Set
From the 1st Netflix prize (won by BellKor)
Jacobs Lab: Computational Cognition Cheat Sheets
Over the years, we've written several notes providing brief introductions to computational methods that are often useful in the study of human cognition. Many undergraduate and graduate students have told us that these notes are extremely helpful.
* Backpropagation Algorithm
* Bayesian Estimation
* Bayesian Statistics: Beta-Binomial Model
* Bayesian Statistics: Normal-Normal Model
* Conditional Independence, Dependency-Separation, and Bayesian Networks
"CBLL, Research Projects, Computational and Biological Learning Lab, Courant Institute, NYU"
We are developing a new type of relational graphical models that can be applied to "structured regression problem". A prime example of structured regression problem is the prediction of house prices. The price of a house depends not only on the characteristics of the house, but also of the prices of similar houses in the neighborhood, or perhaps on hidden features of the neighborhood that influence them. Our relational regression model infers a hidden "desirability sruface" from which house prices are predicted.
[0908.4425] Geometry of the restricted Boltzmann machine
"The restricted Boltzmann machine is a graphical model for binary random variables. Based on a complete bipartite graph separating hidden and observed variables, it is the binary analog to the factor analysis model. We study this graphical model from the perspectives of algebraic statistics and tropical geometry, starting with the observation that its Zariski closure is a Hadamard power of the first secant variety of the Segre variety of projective lines. We derive a dimension formula for the tropicalized model, and we use it to show that the restricted Boltzmann machine is identifiable in many cases. Our methods include coding theory and geometry of linear threshold functions."
[0909.0844] High-Dimensional Non-Linear Variable Selection through Hierarchical Kernel Learning
We consider the problem of high-dimensional non-linear variable selection for supervised learning. Our approach is based on performing linear selection among exponentially many appropriately defined positive definite kernels that characterize non-linear interactions between the original variables. To select efficiently from these many kernels, we use the natural hierarchical structure of the problem to extend the multiple kernel learning framework to kernels that can be embedded in a directed acyclic graph; we show that it is then possible to perform kernel selection through a graph-adapted sparsity-inducing norm, in polynomial time in the number of selected kernels. Moreover, we study the consistency of variable selection in high-dimensional settings, showing that under certain assumptions, our regularization framework allows a number of irrelevant variables which is exponential in the number of observations. Our simulations on synthetic datasets and datasets from the UCI repository show state-of-the-art predictive performance for non-linear regression problems.
MIT Media Lab: Reality Mining
Mobile phones (and similarly innocuous devices) are used for data collection, opening social network analysis to new methods of empirical stochastic modeling.
The original Reality Mining experiment is one of the largest mobile phone projects attempted in academia. Our research agenda takes advantage of the increasingly widespread use of mobile phones to provide insight into the dynamics of both individual and group behavior. By leveraging recent advances in machine learning we are building generative models that can be used to predict what a single user will do next, as well as model behavior of large organizations.
How FlightCaster Squeezes Predictions from Flight Data » Data Wrangling Blog
FlightCaster strikes me as a great example of the next generation of web applications that will leverage that data: bootstrapped startups that apply machine learning and data processing at scale to solve a focused problem people actually care about.
CodeProject: Neural Network for Recognition of Handwritten Digits. Free source code and programming help
So, there are two guys that can make neural nets work: Yann LeCunn and patent attorney Mike O'Neill.
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