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
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.
Can anybody explain me how OPENCV fisherfaceclassifier works?
How it recognize the difference faces ?
It's important to understand that all OpenCV algorithms (usually) are based on a research papers or topics that can be researched and understood.
For FisherFace you can read about the background of it here to understand exactly how it works, this article discussed the background and implementation.
As for implementation of the Fisherface using (as your question is a little unclear) OpenCV provide a tutorial on how to get up and running, they also explain some background and importantly link out too all the underlying papers and research
I didn't really think this question is appropriate for SO as you really could have just googled your question and come to the same answer. I just thought it might be useful to understand how OpenCV is built up.
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.
The problem statement is kind of vague but i am looking for directions because of privacy policy i can't share exact details. so please help out.
We have a problem at hand where we need to increase the efficiency of equipment or in other words decide on which values across multiple parameters should the machines operate to produce optimal outputs.
My query is whether it is possible to come up with such numbers using Linear Regression or Multinomial Logistic Regression algorithms, if no then can you please specify which algorithms will be more suitable. Also can you please point me to some active research done on this kind of problem that is available in public domain.
Does the type of problem i am asking suggestions for comes in the area of Machine Learning ?
Lots of unknowns here but I’ll make some assumptions.
What you are attempting to do could probably be achieved with multiple linear regression. I have zero familiarity with the Amazon service (I didn’t even know it existed until you brought this up, it’s not available in Europe). However, a read of the documentation suggests that the Amazon service would be capable of doing this for you. The problem you will perhaps have is that it’s geared to people unfamiliar with this field and a lot of its functionality might be removed or clumped together to prevent confusion. I am under the impression that you have turned to this service because you too are somewhat unfamiliar with this field.
Something that may suit your needs better is Response Surface Methodology (RSM), which I have applied to industrial optimisation problems that I think are similar to what you suggest. RSM works best if you can obtain your data through an experimental design such as a Central Composite Design or Box-Behnken design. I suggest you spend some time Googling these terms to get your head around them, I don’t think it’s an unmanageable burden to learn how to apply these with no prior experience in this area. Because your question is vague, only you can determine if this really is suitable. If you already have the data in an unstructured format, you can still generate an RSM but it is less robust. There are plenty of open-access articles using these techniques but Science Direct is conveniently down at the moment!
Minitab is a software package that will do all the regression and RSM for you. Its strength is that it has a robust GUI and partially reflects Excel so it is far less daunting to get into than something like R. It also has plenty of guides online. They offer a 30 day free trial so it might be worth doing some background reading, collecting the tutorials you need and develop a plan of action before downloading the trial.
Hope that is some help.