Login [Register]
Don't have an account? Register now to chat, post, use our tools, and much more.
Hello everyone Smile I'm looking to perform a binary classification (either 0 or 1) on a large sample of data (around 5 million points). The data is numerical; i.e. [1,4,6,2,3,0,3,...], and I would like to be able to train a model to accept multiple samples of pre-classified data; which would then be able to generate a prediction on an entirely new set of data. I've been looking into SVM types of learning; however it seems this may not be exactly what I want. Any suggestions or advice would be much appreciated! Smile I'd be happy to answer any questions you may have.
This and this might be useful, especially the second one.

Perceptrons are exceedingly easy to implement and are very useful. Multi-layer perceptrons are great, especially if your data is not linearly separable.

Neural networks (depending of your algorithms) arenít as easy to implement, but they can be even more accurate. There are many different kinds, but I recommend a feed-forward backpropagating neural network. Itís relatively simple, and should get the job done adequately.
Register to Join the Conversation
Have your own thoughts to add to this or any other topic? Want to ask a question, offer a suggestion, share your own programs and projects, upload a file to the file archives, get help with calculator and computer programming, or simply chat with like-minded coders and tech and calculator enthusiasts via the site-wide AJAX SAX widget? Registration for a free Cemetech account only takes a minute.

» Go to Registration page
Page 1 of 1
» All times are GMT - 5 Hours
You cannot post new topics in this forum
You cannot reply to topics in this forum
You cannot edit your posts in this forum
You cannot delete your posts in this forum
You cannot vote in polls in this forum