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37 lines
2.4 KiB
Plaintext
37 lines
2.4 KiB
Plaintext
Cost-Constrained Feature Optimization (CCFO) for MATLAB
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This MATLAB code may be used to replicate the results published in the following manuscript:
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C.R. Ratto, C.A. Caceres, H.C. Schoeberlein, "Cost-Constrained Feature
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Optimization in Kernel Machine Classifiers," IEEE Signal Processing
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Letters, 2015.
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The code is organized into three directories as follows:
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Urban Land Cover/ - Code for replicating the urban land cover experiment from the paper.
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experiment_urbanLandCover.m - Script for running the experiment
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featureTimes_urbanLandCover.m - Function for estimating feature computation times
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MNIST/ - Code for replicating the MNIST experiment from the paper
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experiment_mnist.m - Script for running the experiment
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extractFeaturesFromMNIST.m - Function for extracting features from the handwritten digits
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PRT Plugins/ - The actual machine learning code, written as a plugin to the Pattern Recognition Toolbox (PRT)
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prtClassJCFO - Joint Classifier and Feature Optimization
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prtClassCCFO - Cost Constrained Feature Optimization
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To run either experiment, or to use our code in your own research, you must download and install the
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Pattern Recognition Toolbox (PRT) for MATLAB at http://covartech.github.io/
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To run the urban land cover experiment, you must download the data from the UCI machine
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learning repository: https://archive.ics.uci.edu/ml/datasets/Urban+Land+Cover
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The code benchmarks each of the feature computation times on a test image of a black and white circle.
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The function "MidpointCircle.m" required to generate the test image for doing this is available via Matlab Central:
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http://www.mathworks.com/matlabcentral/fileexchange/14331-draw-a-circle-in-a-matrix-image/content/MidpointCircle.m
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*******************************************************************************************************************
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This software is Copyright 2015 The Johns Hopkins University Applied Physics Laboratory LLC
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All Rights Reserved
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This software is licensed to you under the terms of the Eclipse Public License, Version 1.0,
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a copy of which can be found at http://opensource.org/licenses/EPL-1.0. Redistribution,
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review, modification, and/or use of the software, in source and binary forms are ONLY permitted
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provided you agree to and comply with the terms and conditions set forth in the license.
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******************************************************************************************************************* |