Search Results for "clustering algorithm matlab" - Page 8
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Showing 182 open source projects for "clustering algorithm matlab"
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CRFasRNN
Semantic image segmentation method described in the ICCV 2015 paper
... the 2D outline of objects. Currently we have trained this model to recognize 20 classes. This software allows you to test our algorithm on your own images – have a try and see if you can fool it, if you get some good examples you can send them to us. CRF-RNN has been developed as a custom Caffe layer named MultiStageMeanfieldLayer. Usage of this layer in the model definition prototxt file looks the following. Check the matlab-scripts or the python-scripts folder for more detailed examples. -
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iClust is an information-based clustering algorithm, based upon methods and approaches described in the paper "Information-based clustering" by Noam Slonim
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MIRtoolbox is a Matlab toolbox dedicated to the extraction of musical features from audio files, statistical analysis, segmentation and clustering. Through a user-friendly syntax, low and high-level operators can be combined into complex flowcharts.
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nBoost is a suite of boosting algorithms designed to solve binary classification problems on data that is not linearly separable by a convex combination of base hypotheses, i.e. noisy data. WARNING: Active development. Underlying algorithm is unstable.
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UANA (Universal Abstract Numerical Algorithm) is a c++ api for developers to write algorithms independant of the underlying data container classes.
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This project collect several data mining methods, such as: 1) clustering methods; 2) biclustering methods; 3) FCA methods and etc.
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Hill Finder
Detection of Proximal RNA Binding Sites in HITS Data
... to each other, which would be found when analyzing high-throughput data for enzymes whose activity require multiple RNA binding sites, as well as for overlapping distinct ncRNA loci derived from processing events. We present a density clustering approach based on the OPTICS algorithm which can detect any number of RNA binding sites indicated by the data without prior knowledge of the expected length or number of binding sites in the transcript.
