curious1212

In [0] the author uses dynamic scope (variables with dynamic scope are called special variable in common lisp) to help design a test system in common lisp.

Quoting the author: This is exactly the kind of problem dynamic variables were designed to solve.

Imagine that one of the test cases failed and you need to track down the problem. If you create a dynamic variable that each test function binds to the name of the function before calling check, then report-result can use it without check having to know anything about it.

I think that the main idea is that dynamic gives access to variables and functions defined in an upper level, something like creating classes and subclasses dynamically and they only live during a function evaluation.

[0] http://www.gigamonkeys.com/book/practical-building-a-unit-te...

my take on this: The high bias, low variance means that the models used in the book are the standard used in physic. The low variance means that the author doesn't use any artificial model or parameter except those that arise from physical considerations. Care is taken to motivate physically ML models and those far from physic can't be used with many parameters (hyperparameters).

I don't think the general property of expander graphs is needed, perhaps a relaxed property, such as the following definition:

A graph G is a distance(k)-expander if #edges(U, V-U) > c min(#U,#(V-U)) where U is a set of diameter <=k. Now study the embeding.

I am also curious about what happens if one use SVM to classify vertices of an expander graph: Let G be an expander graph and let u1,u2 be vertices of G such that d(u1,u2) = diameter of G = an even number 2k. We classify the vertices of G like this: f(u) = 0 if d(u,u1)<=k, f(u)=1 if d(u,u1)>=1, then using SVM applied to the vertices encoded by the rows of the adjancecy matrix. I would expect the SVM algorith to have some special property but I don't know what to expect, the best or worst classification, or the support vectors can gives us some information about symmetries in the graph.