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For a course term project we have to build a machine learning algorithm in which user fills the form and the algorithm analyse the best suitable university based on the responses. I am new in the field of machine learning and I do not know what kind of algorithm can we use. Is the recommendation systems a right approach for this?
I did some review on the internet for some similar projects, however still can not find a good resource.
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I am looking for the machine learning correct approach for predicting the lottery numbers, not the most accurate answer but at least we have some predicted output. I am implementing the regression based and neural network models for this. Is their any specific approach which follows this?
It is impossible. The lottery numbers are random - actually to be more specific, the system is chaotic. You would require the initial configuration (positions etc) to insane (possibly infinite) precision to be able to make any predictions. Basically, don't even try it.
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Could someone kindly explain to me in layman terms, or refer me to any articles, why when doing machine learning (e.g. classification model) sometimes we need to turn continuous numeric feature into nominal?
Also, are there times where doing so is not a good idea...e.g. can cause overfitting...or?
Thanks
Wes
This article has a nice explanation
8 Ways to deal with Continuous Variables in Predictive Modeling
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I am bit confused when people talk about text mining and topic modelling randomly in the discussions. Can anyone please explain the exact meaning of these two and their differences.
Text mining is a broad topic.
Topic modeling is one possible subtopic of text mining.
For further details, please see Wikipedia.
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Does linked data applications use tagging for easier information retrieval? Where to get information on this specific topic?
For semantic annotation (tagging) the following applications would be good starting points:
http://gate.ac.uk/
http://www.ontotext.com/kim
Especially the GATE system includes a lot of information and tutorials related to both POS-tagging and ontology-based semantic tagging.
And yes, once your text has been semantically tagged, it is much easier to connect it to other pieces of text using the extra semantic medatada.
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I saw that this company Mitek made an app that could read receipts through the iPhone's camera and record the information from them. (http://www.miteksystems.com/OOMPH_MobileReceipt.asp). My assumption would be that they run the images through some kind of image/text recognition software. Any ideas what what they might be using? If theres an API that can interpret text from pictures that would be great to.
cheers,
Mike
They are probably using some type of OCR software. It is hard to tell exactly which.
You can look at ocropus, for example