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Closed 9 years ago.
I have 130 objects.Each object is defined by 13 points(2-d points),these 13 points form data_unit. Thus there are 130 data_units. I want to classify these data_units into 4 classes. How can we do this.k-means is not possible in this scenario what are the alternatives.
There is a whole set of classification methods based on technique called machine learning. The ones implemented in OpenCV are described here. You can try for example Support Vector Machines. Its a nice and fairly easy in use method, with some tricks to get past data that cannot be linearly separated.
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Closed 9 years ago.
I have numeric data which has missing values. I want to classify the data using Naive Bayes Classifier in Weka but the option is grayed out. Please help.
If a classifier is grayed out, it means that it doesn't support the kind of task you want to do. This could be because of the type of features or because of the type of predictions you want to make.
In your case it could be that you're trying to predict a numeric instead of a categorical attribute.
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Closed 10 years ago.
I don't know if the question is well suited for stackoverflow but also it's hard to fit it to image processing.
Question is about lighting setups in computer vision systems.
Can someone provide reference to materials about lighting setups. When to use backlight, darkfield etc. How to improve features detection by lighting tricks.
I know that good lighting makes image-processing easier and want to get familiar with theory about it.
Transform your image into the Lab color space. The l channel represents the lighting.
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Closed 10 years ago.
I have installed mahout and test run several examples; would it be safe to say that one of Mahout's advantages it being used for unstructured data (scalability aside)?
No... most of the algorithms take fairly specifically structured input like vectors or user-item pairs. There are some tools for vectorizing text docs which are unstructured. But no I wouldn't call it a tool primarily for unstructured data.
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Closed 10 years ago.
I used Mahout, it is great , but need something better that have more implemented algorithms and maybe faster
On the OS side there's WEKA, with a large collection of algorithms across learning disciplines.
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Closed 11 years ago.
I'm looking for a programming language that would scale well on multiprocessors and distributed systems, and is able to work well with the GPU for number crunching.
What do you think, is Erlang and CUDA a good match?
LE: I want to use it for image processing: feature detection, bundle adjustment and scene reconstruction; so it's fairly parallel. The GPU would do the computational intensive part and Erlang would just manage the tasks and shuffle data around.