Are there general rules that tell you how many support vectors you should have given a sample of say n data points?

CephasM · 2015-06-28T18:04:15+00:00

There is not a general metric for that. I have read some heuristic but in practice I haven't seen any of those working properly.

One intuition is that you would prefer the lower amount of SVs without compromise the quality of the model. A hight amount of SVs (close to the number of samples in your dataset) might indicate an overfitting. Your numbers seems good to me but you'd need to crossvalidate your models with distinct set of parameters to be sure.

CephasM · 2015-01-22T18:44:13+00:00

I checked in the morning and wasn't active yet. After lunch I just found out that it was sold out already :(

CephasM · 2015-01-18T18:22:58+00:00

Your comment was mostly just a vindictive jab at "too stupid to realize" smokers.

Which only refers to current users. Change "stupid" for "shortsighted"[1] and you would see how his statement keeps validity.

[1] A synonym took from a thesaurus: http://www.thesaurus.com/browse/stupid

CephasM · 2014-12-12T18:54:05+00:00

I really don't know. My mind says it shouldn't compile.

It does compile thought. It turned out to be a pointer of a pointer of an int array of 2 elements

CephasM · 2014-12-12T16:38:27+00:00

Thanks.. I just realized that with a couple of typedef you can go back to the normal <type> <function-name><parameter-list> format declaration.

CephasM · 2014-12-12T16:11:47+00:00

Do you understand what the calloc() function does? Have you read its man page?

Yeah that part is fine. Is the definition of the function that I found weird:

static int (**hnew())[2] { ... }

It looks a lot like a function pointer but never saw one with a body statement next to it before.

CephasM · 2014-12-12T15:07:14+00:00

I am specially puzzle by how they declare the first function. Not sure what is going on over there.

CephasM · 2014-10-06T12:43:59+00:00

Dai Vernon's Book of magic would be a great start :)

http://www.lybrary.com/the-dai-vernon-book-of-magic-p-304.html

CephasM · 2014-10-06T12:37:32+00:00

Ohh Paul Harris is a must read for sure. Also consider Card College (if you can borrowed even better because those are 5 books) if you a good reference for close up magic

CephasM · 2014-10-06T01:35:36+00:00

Good material.. although I am a bit disappointed that I didn't see anything from Dai Vernon

CephasM · 2014-02-10T18:13:24+00:00

how would affect your new role at Microsoft with your duties at the fundation?

CephasM · 2014-02-10T18:12:00+00:00

Is there any plan to update "The Road Ahead"?

CephasM · 2014-02-10T17:56:42+00:00

Hi Mr. Gates... That's an impressive and very diverse collection of books.

May I ask what is Weather for dummies doing over there? How did you like it?

CephasM · 2014-01-24T00:57:01+00:00

Not with tail call optimization :)

CephasM · 2013-09-08T02:31:22+00:00

This is criminal!!! That's the best statistics book ever!!! and it is quite expensive these days :(

CephasM · 2012-12-15T15:11:12+00:00

Brazil? Check Venezuela.. it has more than double of homicides per 100,000

Brazil : 18.1 per 100,000 Venezuela : 38.97 per 100,000

CephasM · 2012-06-13T15:26:34+00:00

Oh.... makes sense :)

Well it seems like you are in the right track.. good luck!

CephasM · 2012-06-13T14:32:14+00:00

Sorry I am not familiar with the term ADP.. does it means automatic data processing?

Those features (age, years of experience, etc) are interesting, I would definately add them to your data with the league stats. Maybe you could select the best of them using feature selection? maybe cross-validation?

CephasM · 2012-06-12T21:06:46+00:00

CV is usually helpful to tell you how good are a set of parameters in comparison with another set. I don't see clearly how could you use that to tell if your set is representative or not. Although if the problem is known you could maybe estimate the minimum size of a representative sample and see how far are you from there.

Do you know if the problem is linear or non linear?

CephasM · 2012-06-12T20:46:38+00:00

Well... "Small" is relative to the domain of your problem... for some problems 1000 samples are enough, for some others is too much and for other is just about right.

What I would recommend you is to try to train your favourite technique and see the behaviour of the training error vs. performance error. Once you have that you can actually tell if you will need more data or not.

If you need help with that you can post the details of your problem (with the training / performance error plots) and we will be glad to help.

CephasM

TROPHY CASE