pattern-recognition

I am doing a community website that requires me to calculate the similarity between any two users. Each user is described with the following attributes: age, skin type (oily, dry), hair type (long, short, medium), lifestyle (active outdoor lover, TV junky) and others. Can anyone tell me how to go about this problem or point me to some resources? George Dontas Another way of computing (in R ) all the pairwise dissimilarities (distances) between observations in the data set. The original variables may be of mixed types. The handling of nominal, ordinal, and (a)symmetric binary data is achieved

Is there any specific algorithm for handwriting recognition? The algorithm should recognize the hand written letter. Any one could help would be greatly appreciated in advance. Thank you You can use a genetic algorithm: http://www.foibg.com/ibs_isc/ibs-02/IBS-02-p03.pdf You can use Greedy Point Match: http://www.cs.berkeley.edu/~fateman/msw/GreedyPointMatchWriteup.pdf I would suggest reading this paper: On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey OCR might be a good starting point. There's the dollar family of recognizers which you can use to recognize single or

I understand how neural networks work, but if I want to use them for image processing like actual character recognition, I can't understand how can I input the image data to the neural net. I have a very big image of an A letter. Maybe I should try to get some info/specifications from the image and then use a vector of values of that specification? And they will be the input for the neural net? Who has already done such a thing, can you explain how to do this? The easiest solution would be to normalize all of your images, both for training and testing, to have the same resolution. Also the

Is there any open source audio fingerprinting solution that provides a "Shazam like" solution? Shazam uses the commercial solution from Landmark digital but I am looking for an alternative (even if inferior in accuracy and performance) open source solution. Any one knows anything about such implementation or even of a published not implemented algorithm? echoprint looks like a solid alternative to commercial libs. for speech recognition: CMUSphinx OpenEars we only tested echoprint and so far so good. I know the answer is a bit late but as this page pops up in google, we may as well improve it

What would be the best approach (performance-wise) in solving this problem? I was recommended to use suffix trees. Is this the best approach? Have a look at http://en.wikipedia.org/wiki/Suffix_array as well - they are quite space-efficient and have some reasonably programmable algorithms to produce them, such as "Simple Linear Work Suffix Array Construction" by Karkkainen and Sanders user1071840 Check out this link: http://introcs.cs.princeton.edu/java/42sort/LRS.java.html /************************************************************************* * Compilation: javac LRS.java * Execution: java

The end goal would be to see if contains . the compare needs to support minor distortion, scaling, color differences, rotation, and brightness differences. it can be in any language really. i will be running this algorithm as a webservice so its no problem if i have to write this portion in c, c++, python, etc. You should probably take a look at OpenCV and VLfeat . Object detection can be performed for example using Rapidminer IMMI (image mining extension for one of the leading open-source data-mining platform) BoofCV (using SURF feature detection) How about ImageMagick ? Its not a library per

By processing a time series graph, I Would like to detect patterns that look similar to this: Using a sample time series as an example, I would like to be able to detect the patterns as marked here: What kind of AI algorithm (I am assuming marchine learning techniques) do I need to use to achieve this? Is there any library (in C/C++) out there that I can use? user1149913 Here is a sample result from a small project I did to partition ecg data. My approach was a "switching autoregressive HMM" (google this if you haven't heard of it) where each datapoint is predicted from the previous datapoint