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ivmSoftware4.2 (0, 2018-07-01)
ivmSoftware4.2\eigen-eigen-b23437e61a07.zip (776524, 2015-12-13)
ivmSoftware4.2\ivmSoftware4.2 (0, 2018-07-01)
ivmSoftware4.2\ivmSoftware4.2\D2_D3.m (320, 2013-03-25)
ivmSoftware4.2\ivmSoftware4.2\D3_D2.m (131, 2013-03-18)
ivmSoftware4.2\ivmSoftware4.2\X.mat (1176, 2007-08-07)
ivmSoftware4.2\ivmSoftware4.2\check_input.m (1334, 2012-08-03)
ivmSoftware4.2\ivmSoftware4.2\compute_kernel.cpp (1708, 2011-05-11)
ivmSoftware4.2\ivmSoftware4.2\compute_kernel.mexw64 (11264, 2015-12-14)
ivmSoftware4.2\ivmSoftware4.2\dna.mat (63859, 2012-06-01)
ivmSoftware4.2\ivmSoftware4.2\eigen (0, 2018-07-01)
ivmSoftware4.2\ivmSoftware4.2\eigen\.hg_archival.txt (94, 2010-02-12)
ivmSoftware4.2\ivmSoftware4.2\eigen\.hgignore (170, 2010-02-12)
ivmSoftware4.2\ivmSoftware4.2\eigen\.hgtags (472, 2010-02-12)
ivmSoftware4.2\ivmSoftware4.2\eigen\.krazy (42, 2010-02-12)
ivmSoftware4.2\ivmSoftware4.2\eigen\CMakeLists.txt (3664, 2010-02-12)
ivmSoftware4.2\ivmSoftware4.2\eigen\COPYING (35147, 2010-02-12)
ivmSoftware4.2\ivmSoftware4.2\eigen\COPYING.LESSER (7639, 2010-02-12)
ivmSoftware4.2\ivmSoftware4.2\eigen\CTestConfig.cmake (548, 2010-02-12)
ivmSoftware4.2\ivmSoftware4.2\eigen\Doxyfile (10260, 2010-02-12)
ivmSoftware4.2\ivmSoftware4.2\eigen\Eigen (0, 2018-07-01)
ivmSoftware4.2\ivmSoftware4.2\eigen\Eigen\Array (1156, 2010-02-12)
ivmSoftware4.2\ivmSoftware4.2\eigen\Eigen\CMakeLists.txt (827, 2010-02-12)
ivmSoftware4.2\ivmSoftware4.2\eigen\Eigen\Cholesky (1988, 2010-02-12)
ivmSoftware4.2\ivmSoftware4.2\eigen\Eigen\Core (4710, 2010-02-12)
ivmSoftware4.2\ivmSoftware4.2\eigen\Eigen\Dense (140, 2010-02-12)
ivmSoftware4.2\ivmSoftware4.2\eigen\Eigen\Eigen (35, 2010-02-12)
ivmSoftware4.2\ivmSoftware4.2\eigen\Eigen\Geometry (1269, 2010-02-12)
ivmSoftware4.2\ivmSoftware4.2\eigen\Eigen\LU (634, 2010-02-12)
ivmSoftware4.2\ivmSoftware4.2\eigen\Eigen\LeastSquares (523, 2010-02-12)
ivmSoftware4.2\ivmSoftware4.2\eigen\Eigen\NewStdVector (6454, 2010-02-12)
ivmSoftware4.2\ivmSoftware4.2\eigen\Eigen\QR (2257, 2010-02-12)
ivmSoftware4.2\ivmSoftware4.2\eigen\Eigen\QtAlignedMalloc (934, 2010-02-12)
ivmSoftware4.2\ivmSoftware4.2\eigen\Eigen\SVD (556, 2010-02-12)
ivmSoftware4.2\ivmSoftware4.2\eigen\Eigen\Sparse (2803, 2010-02-12)
ivmSoftware4.2\ivmSoftware4.2\eigen\Eigen\StdVector (5534, 2010-02-12)
ivmSoftware4.2\ivmSoftware4.2\eigen\Eigen\src (0, 2018-07-01)
ivmSoftware4.2\ivmSoftware4.2\eigen\Eigen\src\Array (0, 2018-07-01)
ivmSoftware4.2\ivmSoftware4.2\eigen\Eigen\src\Array\BooleanRedux.h (4167, 2010-02-12)
ivmSoftware4.2\ivmSoftware4.2\eigen\Eigen\src\Array\CMakeLists.txt (128, 2010-02-12)
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Matlab implementation of a revised version of Import Vector Machine classifier (Zhu and Hastie 2005) Table of Contents ================= - Introduction - Installation - Usage - Determining the kernel and regularization parameter Introduction ============ This implementation is based on the Import Vector Machine algorithm of Zhu and Hastie. The algorithm is a sparse, probabilistic and discriminative Kernel Logistic Regression model. It shows similar accuracy like the Support Vector Machines, but has a probabilistic output. The model is also sparser, so that the classification step is faster. Installation ============ On Unix systems, we recommend using GNU g++ as your compiler. On all systems just type 'make' into your Matlab command window to build the mex-files. The implementation uses the Eigen library (http://eigen.tuxfamily.org/dox/). Usage ===== params = init; - initialization of all parameters result = ivm(data, params); Input: - phi (M x N) feature matrix with (M x 1) homogeneous feature vectors of each training data point, that means the first element has to be a 1 - c (N x 1) vector of training labels, valid labels are 1, 2, ..., C - params parameters - phit (optional) (M x N) feature matrix with (M x 1) homogeneous feature vectors of each testing data point, that means the first element has to be a 1 - ct (optional) (N x 1) vector of testing labels, valid labels are 1, 2, ..., C. If labels of test data are unknown, simply use any random values Output: - result: P: probabilities of test data trainTime: training time testTime: testing time model: stored model (see below) confMat: confusion matrix (rows: true label, columns: estimated label) perc: User and Producer accuracy acc: overall accuracy (given in percentage) kappa: kappa coefficient nIV: number of used import vectors testAcc: number of right classified testing points Ntest: number of testing data - result.model indTrain: indices of used training points P: probabilities of train data log: log file of all iterations L: function values of optimization function lambda: regularization parameter err_train: training error params: used params (output from init) trainError: last training error trainTime: training time IV: feature vectors of the import vectors kernelSigma: used kernel parameter lambda: used regularization parameter C: number of classes c: true labels of import vectors S: indices of the import vectors nIV: number of used import vectors alpha: parameters of the decision hyperplane fval: last function value of objective function A toy example is given in the function main_toyExample.m with the Ripley dataset. Determining the kernel and regularization parameter =================================================== The determination of the kernel parameter is done via gridsearch and crossvalidation. Greedy selection of import vectors ================================== We use a hybrid forward/backward strategy, which successively add import vectors to the set (forward step), but also test in each step if import vectors can be removed (backward step). Import vectors tend to lie in the acceptance area of their class labels defined through the decision boundary. Therefore, an import vector, which lies in the incorrect acceptance area is tested to be replaced by the nearest import vector from the correct class (backward II step). Since we start with an empty import vector set, and only add import vectors sequentially, in the first iterations the decision boundary can be very different from their final position. Therefore a removal of import points can lead to a sparser and more accurate solution than only using forward selection steps. Publication of the Import Vector Machine: ========================================= Zhu, Ji / Hastie, Trevor: Kernel Logistic Regression and the Import Vector Machine In: Journal of Computational and Graphical Statistics 14, 1, 2005, pp. 185-205. http://pubs.amstat.org/doi/abs/10.11***/106186005X25619. Roscher, Ribana / Frstner, Wolfgang / Waske, Bjrn: I^2VM: Incremental Import Vector Machines In: Image and Vision Computing 30, 4-5, 2012, pp. 263-278. (Please cite this paper if you use the software)

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