Author Archives: brendano

It is worth contemplating how grand the General Social Survey is. When playing around with the Statwing YC demo (which is very cool!) I was reminded of the very old-school SDA web tool for exploratory cross-tabulation analyses… They have the … Continue reading →

Re So I just wrote this hierarchical kernelized Boltzmann process in Prolog using ed on my iPhone. I can send you the RCS repository. — ML Hipster (@ML_Hipster) July 19, 2012 The best I can do is: I once programmed … Continue reading →

Update Aug 10: THIS IS NOT A SUMMARY OF THE WHOLE PAPER! it’s whining about one particular method of analysis before talking about other things further down A quick note on Berg-Kirkpatrick et al EMNLP-2012, “An Empirical Investigation of Statistical … Continue reading →

There was an interesting ICML paper this year about very large-scale training of deep belief networks (a.k.a. neural networks) for unsupervised concept extraction from images. They (Quoc V. Le and colleagues at Google/Stanford) have a cute example of learning very … Continue reading →

The Dice similarity is the same as F1-score; and they are monotonic in Jaccard similarity. I worked this out recently but couldn’t find anything about it online so here’s a writeup. Let \(A\) be the set of found items, and … Continue reading →

Cosine similarity, Pearson correlations, and OLS coefficients can all be viewed as variants on the inner product — tweaked in different ways for centering and magnitude (i.e. location and scale, or something like that). Details: You have two vectors \(x\) … Continue reading →

The idea for a shared task on web parsing is really cool. But I don’t get this one: Shared Task – SANCL 2012 (First Workshop on Syntactic Analysis of Non-Canonical Language) They’re explicitly banning Manually annotating in-domain (web) sentences Creating … Continue reading →

To the delight of those of us enjoying the ride on the anti-power-law bandwagon (bandwagons are ok if it’s a backlash to another bandwagon), Cosma links to a new article in Science, “Critical Truths About Power Laws,” by Stumpf and … Continue reading →

When possible, I like to use R for its really, really good statistical visualization capabilities. I’m doing a modeling project in Python right now (R is too slow, bad at large data, bad at structured data, etc.), and in comparison … Continue reading →

This is fun. Pointwise Mutual Information (e.g. Church and Hanks 1990) between two variable outcomes \(x\) and \(y\) is \[ PMI(x,y) = \log \frac{p(x,y)}{p(x)p(y)} \] It’s called “pointwise” because Mutual Information, between two (discrete) variables X and Y, is the … Continue reading →