As a part of my assignment, I have to work on both HDBSCAN and OPTICS clustering technique. I have researched on many sites to identify the difference between these algorithms. All I got was OPTICS algorithm is a slight variation from HDBSCAN. I would like to know more about this algorithm. Can someone help me to understand the difference between these algorithms and specific use cases about when do we have to use them?
Also, post reference link for further reading. Thanks
Related
I'm learning to find a way to search similar images recently.
There is some popular algorithms in features matching area.For example, Perceptual Hash Algorithm, SIFI and SURF in openCV. I'm wondering that which one is the most accurate.Or is using multiple algorithms a good idea?
Or is there some nice conclusions about the popular algorithms.
Thanks in advance.
There are a lot of algorithms for check similarities, actually matching features.
I searched some algorithms to find the features which are SURF, SIFT, BRISK, LBP, Harris MSER, A-KAZE, FAST and so on.
In many applications, the SIFT is selected to check feature matching. However, I think that you should evaluate the performances algorithm. To find right algorithm for your application.
If you can't evluate the algorithms, I think that using multiple algorithms is better to you.
If you want to check the features, I recommend this link to understand feature extractors, descriptors, matching.
https://kr.mathworks.com/help/vision/local-feature-extraction.html
Thank you.
I would like to get some helpful instructions about how to use the Q-learning algorithm with function approximation. For the basic Q-learning algorithm I have found examples and I think I did understand it. In case of using function approximation I get into trouble. Can somebody give me an explanation through a short example how it works?
What I know:
Istead of using matrix for Q-values we use features and parameters.
Make approximation with the linear combination of feauters and parameters.
Update the parameters.
I have checked this paper: Q-learning with function approximation
But I cant find any useful tutorial how to use it.
Thanks for help!
To my view, this is one of the best references to start with. It is well written with several pseudo-code examples. In your case, you can simplify the algorithms by ignoring eligibility traces.
Also, to my experience and depending on your use case, Q-Learning might not work very well (sometimes it needs huge amounts of experience data). You can try Fitted-Q value for example, which is a batch algorithm.
I and a group of people are developing a Sentiment Analysis Algorithm. I would like to know what are the existent ones, because I want to compare them. Is there any article that have the main algorithms in this area?
Thanks in advance
Thiago
Some of the papers on sentiment analysis may help you -
One of the earlier works by Bo Pang, Lillian Lee http://acl.ldc.upenn.edu/acl2002/EMNLP/pdfs/EMNLP219.pdf
A comprehensive survey of sentiment analysis techniques http://www.cse.iitb.ac.in/~pb/cs626-449-2009/prev-years-other-things-nlp/sentiment-analysis-opinion-mining-pang-lee-omsa-published.pdf
Study by Hang Cui, V Mittal, M Datar using 6-grams http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.83.5942&rep=rep1&type=pdf
For quick implementation naive bayes is recommended. You can find an example here http://nlp.stanford.edu/IR-book/
We did a statistical comparision of various classifiers and found SVM to be most accurate, though for a dataset consisting of large contents
( http://ai.stanford.edu/~amaas/data/sentiment/ ) none of the methods worked well.Our study may not be accurate though. Also instead of treating sentiment analysis as a text classification problem, you can look at extraction of meaning from text, though I do not know how successful it might be.
apparently the NLTK, a python natural language processing library, has one:
http://text-processing.com/demo/sentiment/
Probably worth having a look at it.
I'm a self-taught developer and, quite frankly, am not all that great at figuring out which search or sort algorithm to use in any particular situation. I was just wondering if there was a Design Patterns-esque listing of the common algorithms available out there in the ether for me to bookmark. Something like:
Name of algorithm (with aliases, if any)
Problem it addresses
Big-O cost
Algorithm itself
Examples
Other algorithms it may be used with/substituted for
I'm just looking for a simple, concise listing of the algorithms I probably should know in one location. Is there anything like this available?
The web site http://www.sorting-algorithms.com/ shows many popular sorting algorithms, and describes their complexity and implementation. It goes the extra step to show, via animations, how those algorithms perform on different types of data (i.e pre-sorted, sparse, reverse-sorted, etc...).
This site has some examples of sorting algorithms, included visual aids to help you get the hang of it. I personally like the various best/worst/average/few unique cases they show.
Wikipedia has a nice table that lists most of the common sorting algorithms along with classification of them and basic analysis of their complexity characteristics.
The more common sorting algorithms have pseudocode and more in-depth analysis. For less common sorting algorithms, you'll probably have better luck finding details in academic papers or real implementations.
your should read CLRS.
In terms of problems variety, there are millions. and it all comes from puzzles and math.
Skienna has nice problems with different varieties.
You have a great article on the wikipedia.
http://en.wikipedia.org/wiki/Sorting_algorithm#Comparison_of_algorithms
But I would suggest reading some book. Almost every book has one chapter about sorting.
This may be more appropriate for math overflow, but nevertheless:
Given a 3D structure (for example, a molecule), what is a good approach/algorithm to find symmetry (rotational/reflection/inversion/etc.)?
I came up with brute force naïve algorithm, but it seems there should be better approach.
I am not so much interested in genetic algorithms as I would like best symmetry rather then almost the best symmetry
there is this here: http://pubs.acs.org/doi/abs/10.1021/ci990322q from my field. would be good to know what mathematicians/computer science people came up with as well.
A link to website/paper would be great. Thanks
This paper should get you started:
http://graphics.stanford.edu/~niloy/research/approx_symmetry/paper_docs/approx_symmetry_sig_06.pdf
See this website for Symmetry Detection and Structure Discovery research. The papers at the bottom include the one that #Xavier Ho mentions.