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What is the fastest classification algorithm?
If I have a very large data set and a very large number of features and I need to conduct classification on it within about an hour.
What would be the fastest classification algorithm?
What would be the fastest classification algorithm?
It would depend on the nature of your data, size and dimensions.
Moreover, it would depend on the trade-off you want to achieve between speed and accuracy.
There is no single best algorithm for all cases.
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When i have high resolution image. (512,512,3) or (150,150,3) which input shape outperform?
In general it depends on what you want to achieve and how you define "outperform".
(512, 512, 3) will lead to better performance on some kind of metric like. accuracy, IoU ... (depending on the task), (150, 150, 3) will lead to worse performance but better runtime and less memory consumption.
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I have problems with certain algorithmic terms.
What is a robust algorithm ?
What is a resistant algorithm ?
What is a resilient algorithms ?
Thank you in advance.
These attributes have no exact definition. So it depends on your topic/problem what they mean.
They are all used to describe algorithms that can cope with some kind of errors (e.g. outlier or noise) in the input-data and still deliver a useful / the expected result.
So in general you define the kind of errors the algorithm is expected to handle in a defined way.
E.g 'This algorithm returns for an input with less than 5% outlier a result with an accuracy of 99%.'
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I'm researching on compression algorithms (huffman coding and LZ77) and was wondering how I would evaluate their efficiency depending on the input image. I know how they work but I can't find information on their evaluation (mathematically). Thanks!
General-purpose (universal) compressors like LZ77 are usually compared by testing them against a standard set of sources and comparing the results, see: http://www.maximumcompression.com/, http://www.maximumcompression.com/data/summary_mf.php, for example.
Compressors for specific purposes are tested against source sets that are chosen to be as representative as possible.
For some applications it is also useful to place mathematical bounds on compression efficiency in terms of the source entropy.
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The Myers diff algorithm performs well when the differences between the two texts are small, because most simple implementations have complexity O((N+M) * D). However when differences are large, it takes a very long time to run. For example, if one of the texts is large and the other is the empty string, many implementations take several minutes to run.
If you knew the differences were large, then you could choose a different algorithm. How do diff tools make this determination in practice?
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What kind of data structure should be used for nearest neighbor searching in 2d dimension?
I have searched and found out that there are many data structures for this: k-d tree, quadtree, octree.
So what kind of structure should I use?
I suggest a R-Tree, it's designed for that purpose.