I refer to the stackoverflow question at this link regarding calculation of scores at the leaves by xgboost algorithm, once again. I have searched documentation a lot but could not find any intuitive explanation. Is it possible to have an intuitive meaning as to scores at the leaves in this picture and how (intuitively) these could have been calculated. Unfortunately, it is difficult for me to understand mathematics behind all this.
We are trying to document a Health Assessment process. An assessment is considered complete when we have received, and committed to a database, the lab results from a blood draw, the biometric data from an examination and the answers from a questionnaire to be completed online OR on paper. The biometrics are recorded the same day as the blood draw. The employee can complete the questionnaire anytime in the process. We have no control over the length of time the lab results take to get back. We do not give the employee an end-date for completing the questionnaire. They can do it before or after the exam.
It sounds like a simpler version of the process described in this question, but I was hoping someone could point me to an online example; I'm having a hard time wrapping my head around it visually. If it matters, I'm using Adonis Community Edition.
I have made a few assumptions here, because your description of your process is not 100% clear to me. However, you might want to model something similar to this:
I am trying to analyse online code and want to use Bayesian Classification. However I need a fair amount of pre classified code as sample data.
Maybe the twenty or so top languages?
Does anyone know of such a corpus?
there was a data set on Kaggle with questions from StackOverflow where the objective was to guess the tags related to the question. That could require guessing the language of code samples (or just looking for keywords)
https://www.kaggle.com/c/facebook-recruiting-iii-keyword-extraction
Other possibilities searching through Github - since all that code is free and open.
StackOverflow itself shares its own data of all user contributed posts (anonymized)
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I'm looking for a way to determine whether a picture is explicit (is Safe For Work ) or not.
I am currently looking for an API that is capable of doing it, but so far I didn't have any success.
One of the ideas I had was to use the google search API and provide a URL to a picture, and looking whether or not it is in the results when safeSearch is enabled, but it will fail on a picture that was added before the crawler got to it.
Alternatively, I'm looking for pointers regarding what to look for in an image to determine how SFW it is. Any suggestions regarding shapes, colors or patterns?
As promised, a SFW paper from Google researchers and a patent for your study procured from this blog entry.
One of my colleagues led the development of the porn classification technology at one of the largest web companies. I will share what he told me about the development of the filter.
The definition of what is explicit varying greatly among jurisdictions so what is considered explicit in the US might not be in other parts of the world and vice-versa. So models need to take into account the users origin.
A purely imaged based approach is almost impossible to use effectively at web scale. The feature space is very complex in terms of how humans judge what is explicit and what is not and developing appropriate feature extraction technology for images proved to be exceedingly difficult.
Some of the most predictive features are the text on pages that link to the images. These are among the easiest features to develop also.
Building labeled training sets is very difficult since classifying porn and other explicit content for 8 hours a day tends to take a toll on the labelers. Because of this the turn over is fairly high with almost no one lasting a year.
Getting a high accuracy from the classifiers is still very, very difficult. They worked on it with several PhD's and a very experienced team and still did not achieve the accuracy that you are probably looking for.
If you have a more constrained problem space you can probably achieve a higher accuracy. If you are using image features only the algorithm or model will probably not generalize well and will have a high false positive rate. Best of luck.
See papers:
Detection of Pornographic Digital Images
Jorge A. Marcial-Basilio, Gualberto Aguilar-Torres, Gabriel Sánchez-Pérez, L. Karina Toscano-
Medina, and Héctor M. Pérez-Meana
Pornography Detection Using Support Vector Machine
Yu-Chun Lin (林語君) Hung-Wei Tseng (曾宏偉) Chiou-Shann Fuh
Image-Based Pornography Detection
Rigan Ap-apid
De La Salle University, Manila, Philippines
You can also take some hints from existing implementations e.g.:
"The Porn Detection Stick uses advanced image analyzing algorithms that categorize images as potentially harmful by identifying facial features, flesh tone colors, image back grounds, body part shapes, and more."
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I'm looking for ideas for a Neural Networks project that I could complete in about a month or so. I'm doing it for the National Science Fair, so I need something that has some curb appeal as well since it's being judged.
It doesn't necessarily have to be completely new and unique, I'm just looking for ideas, but it should be complex enough that it would impress someone who knows about the field. My first idea was to implement a spam filter of sorts, but I recently found out that NN's aren't a very good way to do it. I've already got a basic NN simulator with Genetic Algorithms, and I'm also adding the the generic back-propagation algorithms as well.
Any ideas?
Look into Numenta's Hierarchical Temporal Memory (HTM) concept. This may be slightly off topic if the expectation is of "traditional" Neural Nets, but it is also an extremely promising avenue for Artificial Intelligence.
Although Numenta introduced HTM and its associated software platform, NuPIC, almost five years ago, the first commercial product based upon this technology was released (in beta) a few weeks ago by Vitamin D. It is called Vitamin D Video and essentially turns any webcam or IP camera into a sophisticated video monitoring system, recognizing classes of items (say persons vs. cats or other animals) in the video feed.
With the proper setup, this type of application could make for an interesting display at the Science Fair, one with much "curb appeal".
To wet your appetite or even get your feet wet with HTM technology you can download NuPIC and check its various sample applications. Chances are that you may find something that meets typical criteria of both geekness and coolness for science fairs.
Generally, HTMs aim at solving problems which are simple for humans but difficult for computers; such a statement is somewhat of a generic/applicable to Neural Nets, but HTMs take this to the "next level".
Although written in C (I think) NuPIC is typically interfaced in Python, which makes it a convenient test bed for simple yet sophisticated proofs of concept applications.
You could always try to play around with a neural network and stock courses, if I had a month of spare time for a neural network implementation, thats what I would play with.
A friend of mine in college wrote a NN to play go on a 9x9 board.
I don't think it ever got very good, but I think it would be fun to try.
Look on how a bidirectional associative memory compare with other classical edit distance algorithms (Levenshtein, Damerau-Levenshtein etc) for typo correction. Also consider the articles on hebbian unlearning while training your NN - it seems that the confabulation phenomena is avoided.
I've done some works on top of NN, mainly an XML based language (Neural XML). See details here
http://amazedsaint.blogspot.com/search/label/Neural%20Network
Also, one interesting .NET Neural network project is Aforge.net - Check out that as well..
You can implement the game Cellz or create a controller for it. It was first created by Simon M Lucas. It's a nice and interesting game, and i'm sure that everyone will love it. I used it also for a school project and it turned out very ok.
You can find in that page some links to other interesting games.
How about applying it to predicting exchange rate (USD - EUR for example for sub minute trading) should be fun to show net gain of money over 1 month.
I doubt this will work for trades longer than a minute... without a lot of extra work.
I like using committee machines so why not apply it to Face-Detection in images / movies or voice print authentication.
Finally you could get it to play pleasing music and use a crowd sourcing fitness function whereby people vote for the best "musicians"