What are some practical applications for a single layer perceptron?

Question

I was having a look at this awesome tutorial on single layer perceptron. I tried the implementation out and it works like charm, but I was wondering if it's there any practical use for it as is (at such a low complexity degree).

Any example?

Answer 1

I think they are used in some sort of spam filters, but darn if I remember the details at this hour...

Answer 2

The more categorical the feature set, the better.

Answer 3

you might want to wait for the multi-layer perceptron tutorial; single-layer perceptrons are incredibly limited - see Perceptrons by Minsky and Papert for an authoritative (and highly mathematical) study of what they can and cannot do.

Answer 4

You can actually do an incredible amount with just a perceptron. For example, many of the theoretical weaknesses of perceptrons can be overcome by moving to a richer feature representation of the data. The most standard way to do this is through kernels. Once you do this, you can then solve many different learning problems through reductions that transform these other problems into binary classification.

One major algorithm is SNOW, which is used by Dan Roth in natural language processing quite heavily

Lecture notes on the use of kernels in the perceptron algorithm can be found here: http://l2r.cs.uiuc.edu/~danr/Teaching/CS446-08/Lectures/04-LecOnline-P3.pdf

The rest of the notes on perceptron and winnow are handy as well: http://l2r.cs.uiuc.edu/~danr/Teaching/CS446-08/lectures.html

A further discussion of kernel perceptrons can be found in this paper: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.48.8200

John Langford (not me, by the way) has done a lot of work in the reductions I mentioned: http://hunch.net/~jl/projects/reductions/reductions.html

Answer 5

The paper Neural methods for dynamic branch prediction describes how perceptrons can be used to predict whether a instruction branch will be taken or not in hardware with an accuracy of over >95%.

Answer 6

A single layer perceptron is really just a fairly inefficient and inaccurate way of finding a least squares solution to a linear system. More efficient methods might use Singular Value Decomposition ( SVD ) to find the pseudoinverse which amounts ( I think ) to the doing the same thing that the single layer perceptron does while learning. But that said, finding least sqaures solutions is a generally useful thing, so in that sense the single layer perceptron is doing something pratical!

Answer 7

Perceptrons are simply a way of determining a (thresholded) linear combination (weighted sum of inputs). In practice, nearly nobody uses the perceptron procedure for learning because it's ridiculously inefficient and not guaranteed to ever find a solution if you choose your learning rate badly.

If you're predicting a real-valued quantity then perceptrons are equivalent to linear regression. If you're interested in binary classification, logistic regression is the tool to use, as it provides you a posterior probability of the likelihood of a given classification, given some (fairly reasonable) assumptions (that your predictive variables are Gaussian distributed, with different means but the same covariance).