How sure are you that large margin implies low VC dimension (and good generalization error)? It is true. But even if you have taken a good course on machine learning you many have seen the actual proof (with all of the caveats and conditions). I worked through the literature proofs […]

Estimated reading time: 3 minutes

As John mentioned in his last post, we have been quite interested in the recent study by Fernandez-Delgado, et.al., “Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?” (the “DWN study” for short), which evaluated 179 popular implementations of common classification algorithms over 120 or so data […]

Estimated reading time: 16 minutes

A fair complaint when seeing yet another “data science” article is to say: “this is just medical statistics” or “this is already part of bioinformatics.” We certainly label many articles as “data science” on this blog. Probably the complaint is slightly cleaner if phrased as “this is already known statistics.” […]

Estimated reading time: 12 minutes

We give a simple explanation of the interrelated machine learning techniques called kernel methods and support vector machines. We hope to characterize and de-mystify some of the properties of these methods. To do this we work some examples and draw a few analogies. The familiar no matter how wonderful is […]

Estimated reading time: 63 minutes

Living in the age of big data we ask what to do when we have the good fortune to be presented with a huge amount of supervised training data? Most often at large scale we are presented with the un-supervised problems of characterization and information extraction; but some problem domains […]

Estimated reading time: 23 minutes

Having a bit of history as both a user of machine learning and a researcher in the field I feel I have developed a useful perspective on the various trends, flavors and nuances in machine learning and artificial intelligence. I thought I would take a moment to outline a bit […]

Estimated reading time: 15 minutes

We describe the “the local to global principle.” It is a principle used to break algorithmic problem solving into two distinct phases (local criticism followed by global solution) and is an aid both in the design and in the application of algorithms. Instead of giving a formal definition of the […]

Estimated reading time: 53 minutes

REPOST (now in HTML in addition to the original PDF). This paper demonstrates and explains some of the basic techniques used in data mining. It also serves as an example of some of the kinds of analyses and projects Win Vector LLC engages in.

Estimated reading time: 37 minutes