I'd like to ask what algorithms have been used in the achartengine? Is Dijkstra's algorithm used in here or any other algorithms? I'm learning algorithms as of now. And if you could give me recommendations where to start, I'd gladly appreciate it. Thank you.
No fancy algorithm has been used in AChartEngine. The only one is for transforming real data to screen coordinates.
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I am a newbie to skilearn.
What I want to do is quite simple - just feed my model with a bunch of similar texts.
Then, I want to be able to give it a new text, and see if it is similar to the existing texts in the dataset.
How should this be done?
Thanks very much in advance.
One good aproach might be using cosine similarity. This is a very good tutorial for starting:
Machine Learning :: Cosine Similarity for Vector Space Models (Part III)
Another good approach would be a Bayesian Classifier, like the ones used for SPAM detection. Take a look at this link to learn more about them.
I want to go through algorithm of C5.0 or C4.5
I want to know how does it works so that I can better predict where it would fetch better result.
I think nowhere. It is a proprietary stuff. Use CART - it is much better.
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 have already tried mean squared error and cross correlation, but they don't give me that much of a good result. I'm doing that for Brain MRI. Thank you.
I have seen principal component analysis used to compare separate brain scan images.
http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5874201
This might be useful, but I am not entirely sure what you are trying to do with similarity between sones.