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Content-based Image Retrieval (CBIR) consists of retrieving visually similar images to a given query image from a database of images. It is done by comparing selected visual features such as color, texture and shape from the image database.
This project applies an interpretation of a k-NN algorithm to a library of GPS commuter data for speed prediction. The overall goal is to lay the foundation for a power management protocol for use in electric vehicles with hybrid energy storage.
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An adaptive neural network and evolutionary algorithms approach to the machinelearning tasks, based on the modular graph grammars. Tested on the "two spirals problem" and other tasks.
Implemented in Matlab and C++.
A graphical MatLab framework for estimating the parameters of, modeling and simulating static and dynamic linear and polynomial systems in the errors-in-variables context with the intent of comparing various estimation strategies.
A MATLAB spectral clustering package to handle large data sets (200,000 RCV1 data) on a 4GB memory general machine. We implement various ways of approximating the dense similarity matrix, including nearest neighbors and the Nystrom method.
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The project goal is to develop several IP cores that would implement artificial neural networks using FPGA resources. These cores will be designed in such a way to allow easy integration in the Xilinx EDK framework.
Open Source code for machinelearning applied to anthropological/art objects. initially a collaboration between C. Wiggins+I. Tamaru (Columbia) and A. Gansell (Harvard) to identify true regions for lost/misplaced objects recovered in mesopotamia (Iraq).
The simpleSVM project contains MachineLearning codes for algorithms based on the SimpleSVM. It provides methods for Support Vector Machines and related methods, such as One-Clas SVM, nu-SVM...
Function
1. classifier_knn
2. accuracy_knn
Description
1. Returns the estimated label of one test instance, the k nearest training instances, the k nearest training labels and creates a chart circulating the nearest training instances (chart 2-D of the first two features of each instance).
2. Returns the estimated labels of one or multiple test instances and the accuracy of the estimates.
GURLS - (Grand Unified Regularized Least Squares) is a software package for training multiclass classifiers based on the Regularized Least Squares (RLS) loss function.
The initial version has been designed and implemented in Matlab. Teh current goal is to implement an object-oriented C++ version to allow for a wider distribution of the library within the open-source developers' comunity.
Main functionalities already implemented are:
* Automatic parameter selection.
* Handle...