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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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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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Can any algorithm that performs automatic learning be called a "machine learning algorithm"? Or is this designation is reserved to the known ML algorithms like SVM, Feature Selection... ?
Any algorithm that learns to do a task by itself and gets better at it is considered machine learning even if it just as simple as computing the joint probability. Only condition is automated learning, that's all.
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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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Sorry that this question is subjective, but given that the Stack Overflow has the largest Z3 user base, I want to give it a try.
I have a big constraint satisfaction problem that consists of many integer propositional logic formulas and a few first order logic formulas that only contain integers(quantifiers). I care very much about the efficiency, because I am building an interactive program synthesizer.
I am now using z3 solver and the check time is sometimes too long. I wonder if z3 is the best tool to tackle the problem I mentioned in above or there is a better tool? How about CPLEX?
Any suggestion will be appreciated.
Edit:
Sorry, the code has been remove for privacy reason. I can email you my code personally if you are willing to take a look. Thanks in advance.
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This is a trivial question - but something I always miss in the day-to-day programming.
Is there a gook lookup reference available for the common algorithms that we usually face in our everyday programming - sorting,sequences,graphs.
The emphasis is more on the applicability and pseudocode ,rather than the mathematical proofs(which I find is what books tend to stress on).
The idea is to keep a ready reference,as and when we need to resort to one of these algorithms into our respective development project and languages.
Dictionary of Algorithms and Data Structures
How about this?
List of algorithms#Wikipedia