I am trying to use MPR as a metric to evaluate my recommendation system based on implicit feedback. Can somebody please explain MPR? I have gone through this paper
However, I can't seem to get an intuitive understanding.
Any help would be appreciated.
EDIT : I went through Microsoft's research on metrics for recommendation engine metrics
It is recommended that MPR is recommended when we're looking for one 'positive' result. Can somebody also explain why that is the case?
EDIT 2 :
Related
I am doing a project at the university and I need to train an algorithm to rephrase sentences, what can you advise for implementation? Is it possible to use a translator to translate into another language in the end to get a paraphrased sentence? Also i want to use Word2Vec, or it's a bad idea?
This kind of broad-advice question – and about a very-tough problem, paraphrasing text, that is still a very active research problem – would be better answered by surveyin the research literature.
A great site for searching relevant papers – and then finding other related papers once you've set some positive examples – is http://www.arxiv-sanity.com/.
Searching for [paraphrasing] or [summarization] would give you a running start in seeing major techniques & their limitations. And, once you start bookmarking papers by the little 'disk' icon, it can autosuggest important related papers... so even if your 1st few finds are tangential or far-from-usefulness, it can lead you to the seminal papers, & prevailing cutting-edge algorithms/libraries, pretty quickly.
Can anyone suggest me a good source to learn?
I am a newbie in ML
As I am a newbie, I have not done anything in this.
This might be an excellent place to start. You can create a new kernel straight from the dataset page, and the data will be ready for you when you enter the kernel. You can also look at other people's kernels who have used that dataset, and I bet you'll find plenty of helpful examples.
You'll get lots of hate for asking this kind of question, since it doesn't fit in S.O. question parameters, but I prefer to be a useful human.
I am trying to Code a genetic algorithm in Matlab but really dont know how it works in images and how to proceed? Is there any basic tutorial that can help me understand how to apply GA on images (starting from 2d to multidimentional images ).
That will be a great help for me.
Thanking everyone in anticipations.
Kind Regards.
For GA you need two things: a fitness function that can evaluate any solution and tell how good it is, and a representation of your solution so that you can do crossover and mutation. Once you have these, you are good to go. I'm not an expert on image processing so I can't help you with that exactly.
Look at the book Essentials of metaheuristics which is a very good resource for start with evolutionary computation (and not only that) in general. It's free.
There is a paper on this subject which you can find at the IEEE library. I believe it solves the problem you vaguely describe.
There are several normalization methods to choose from. L1/L2 norm, z-score, min-max. Can anyone give some insights as to how to choose the proper normalization method for a dataset?
I didn't pay too much attention to normalization before, but I just got a small project where it's performance has been heavily affected not by parameters or choices of the ML algorithm but by the way I normalized the data. Kind of surprise to me. But this may be a common problem in practice. So, could anyone provide some good advice? Thanks a lot!
I am working with OpenCV for a project used for recognition and I had a general question regarding the API and it's terms. I've looked online and couldn't find anything specific to this but I was wondering what the differences were regarding the Discrete Adaboost, Real AdaBoost, LogitBoost, and Gentle AdaBoost. If anyone could direct me to a pros v cons or a general description about these so that I may research which would be useful.
Update
I have added a link to a powerpoint file that goes over the different variations of the Boosting techniques. Hope this hopes someone else out there.
Adaboost powerpoint
Thanks in advance
There isn't really a simple "always use technique X" otherwise there wouldn't be a need for all the others . You really have to understand the details and experiment.
see The opencv discussion and A list of papers and technical summaries