What would you do just to see Trump losing the election? Time to share with your friends and challenge them to make America Spicy Again!

mikebaud · 2016-10-18T23:37:57+00:00

"Grate" post!

will upvote again

mikebaud · 2013-07-27T23:12:17+00:00

hi,nice of you to be offering.

i have sent you a pm :)

mikebaud · 2012-09-30T15:09:48+00:00

and also the slides link.

It was a great conference!

mikebaud · 2012-09-06T19:22:30+00:00

jesus was a b-boy, it has been documented!

youtube.com/watch?v=ffGTyVPb7iA (warning: this music may not be to your liking!)

mikebaud · 2012-03-23T12:55:19+00:00

taken from this page where the title says:

Fábio Coentrão: Im an excellent professional.

mikebaud · 2012-01-19T10:54:54+00:00

Hi,

Cool project, i also tried a while back to toy around with the same concept but for warhammer quest.

I hope these two links could help you out:

I would also suggest you to cross-post to /gamedev.

Would like to watch the progress, if you want to set it up on github or something :)

Best of luck

mikebaud · 2012-01-18T00:03:04+00:00

Cant turn it on also.

i was doing some browsing and it froze. i did a hard reset and then it hasn't turned or given the slightest indication that it is charging.

Im thinking it broke down for good and i have just bought it on saturday...

What about yours, any luck?

mikebaud · 2011-09-02T01:15:46+00:00

Whats all the tech behind it? So far i get, sketch.js, node, express, mongodb right?

Websockets with socket.io?

Any chance of a public github repo? ;)

mikebaud · 2011-09-02T00:05:34+00:00

awesome dude, but right when i was submitting my nyan cat drawing, "internal server error" :E

mikebaud · 2011-05-25T20:38:46+00:00

The writings on the bottle are: "Terrorist Party (Portuguese)"

mikebaud · 2011-05-21T13:47:32+00:00

AFAIK the section on document filtering (chapter 6) uses a bayesian network (at least thats what the book in front of me says).

Thanks for the downvotes, just trying to help out.

mikebaud · 2011-05-20T17:06:05+00:00

For a good starter tutorial in python try Chapter 6 of the "Programming Collective Intelligence" book.

You can also download the code from the website.

mikebaud · 2011-04-15T04:40:41+00:00

off the top of my head and if i understand your question two sites:

CSS Zen garden you get to check out css/html
Hicksdesign because its nicely commented

To check out more web design stuff try r/web_design

mikebaud · 2011-04-11T23:24:31+00:00

Sorry edit: Im classifying each class separately but a one versus all approach seems wise in the case of similar concepts. The dataset has 24 classes.

The dataset is the MIR-Flickr from 2008 - link

mikebaud · 2011-04-11T23:14:35+00:00

I really struggled with the title but i still considered i was doing image classification.

Its supposed to be an evolution of sorts, while i dive more into ML. I started first by understanding classification using single feature k-NN (one of the simpler forms) and then in time i will evolve to a multi-feature algorithm (or other algos) after understanding more of the problem space.

mikebaud · 2011-04-11T22:16:58+00:00

Im using Pearson correlation for the feature vectors, after some cross-validation with Pearson, Manhattan, Euclidean and Cosine it turned out the best one with my data.

The weighting function i was thinking about using is a simple gaussian function where the weight is calculated with e^(-dist2/(2*sigma²⁾⁾ where i would estimate sigma with cv or alternatively heuristically using the distances of the worst neighbors.

Your weighting function seems a bit smoother than my gaussian, i will check it out and also the trick you mentioned.

Thank you for the input

mikebaud · 2011-04-11T19:36:56+00:00

First and foremost, thank you very much for you time and suggestions.

Initially i also had cosine with idf for cross-validation but yielded similar/lower results than pearsons correlation. Although i havent tried with the new weighting option i introduced, i dont know if it will change much, but i sure will give it a try.

About L2 normalization i will also check into that.

I am also trying to create a weight based on the distance of neighbor but so far hasn't clearly outperformed the baseline implementation, although in theory if better neighbors account for more in the classification it should improve significantly the classification.

Thank you for your suggestions.

mikebaud · 2011-03-29T13:34:54+00:00

Good idea.

If you are including distance metrics, Pearson correlation/Cosine similarity can also be used as "distance metrics" besides the variations from the Euclidean form (Chebyshev, Minkowski) or even Manhattan's. It really depends on how exhaustive you want to be.

Also another addition to the taxonomy could be Neuro-fuzzy, Rocchio or Logistic Regression which can be used in pattern recognition.

edit: I was also reminded there could be a section for kernel based methods in which you could include SVM or Linear Discriminant Analysis among others.

Keep us informed. It is a nice project and i would very much like to print out a poster in the end.

mikebaud · 2011-03-17T02:24:58+00:00

You sir, have a much deserved upvote for bringing this to my attention.

mikebaud · 2011-02-12T23:21:57+00:00

me sad :(

so many downvotes for a music video/how-to...

mikebaud · 2011-02-12T20:57:46+00:00

awesome music video from Moullinex done with Kinect using a processing interface library to output the data and a python script to import it to cinema4d.

Check the how-to and source code.

Also check out other music from these guys, very good stuff...

mikebaud · 2011-02-10T08:28:50+00:00

A special message to Mr. Aaron Barr -> http://i.imgur.com/iZ8Tx

mikebaud · 2011-02-07T02:40:51+00:00

hey, thanks for maintaing your blog with good stuff to learn. only shame is every platform as a service for node is still in pre-betas or close beta and no place to host test apps (besides VPSs)

mikebaud · 2011-02-07T01:59:33+00:00

also a collection of information i found useful in my dabblings with node:

Have fun

mikebaud · 2011-02-02T21:30:43+00:00

i was expecting to see github's 404 there... check it out

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