文件列表:

boost_code\01593701.pdf (894941, 2007-09-28)
boost_code\array.m (810, 2007-09-28)
boost_code\F_AssignLabelM2V2a.m (1804, 2007-09-28)
boost_code\F_GetWgtBTW.m (1165, 2007-06-26)
boost_code\F_JD_LDA_PLossVa.m (2562, 2007-09-28)
boost_code\F_PartTrainValid.m (1461, 2007-06-27)
boost_code\F_Random.m (540, 2007-06-26)
boost_code\F_RandPartV4.m (1438, 2007-09-28)
boost_code\F_wJD_LDAV2.m (4700, 2007-09-28)
boost_code\R_JD_LDA_BstTrnM2V1aR.m (7491, 2007-09-28)
boost_code (0, 2009-07-09)

************************************************************************** * Matlab source codes for the Boosting of the J-DLDA learner(B-JDLDA) * * Author: Lu Juwei * * Edited by Tejaswini G * * Bell Canada Multimedia Lab, Dept. of ECE, U. of Toronto * * Released in September 2007 * ************************************************************************** The matlab functions implement the method presented in the paper regarding boosting of the strong J-DLDA learner[01593701.pdf] J. Lu, K.N. Plataniotis, A.N. Venetsanopoulos, S.Z. Li, Ensemble-based Discriminant Learning with Boosting for Face Recognition, IEEE Transactions on Neural Networks, Vol. 17, No. 1, pp. 166-178, January 2006 [desciption of files]: R_JD_LDA_BstTrnM2V1ar.m : Main function to call. Implements the AdaBoost with JD-LDA F_Random: Function to generate random numbers / data F_PartTrainValid: Partition a database into two sets: training set and validation set based on the sample distribution produced during AdaBoosting process. F_JD_LDA_PLossVa: Function to 1. classify the elements of the training set using the individual learner, 2. compute pseudo loss. F_AssignLabelM2V2a: Function to assign labels to test inputs using the AdaBoost learner F_GetWgtBTW: Program to update the weighting matrix used to compute the between class scatter matrix for the individual LDAs, based on classification results of the previous Boosting iteration F_RandPartV4: Program to initially partition the training data into training and validation set, at iteration 1. F_wJD_LDAV2: Program to implement weighted Juwei's D-LDA (wJD-LDA), which uses weighted between-class scatter matrix, the weights (mA) are from the sample distribution. array.m: Program to find the number of distinct elements in an array, and the number of times each distinct element occurs [usage]: Call the function R_JD_LDA_BstTrnM2V1ar() with the required parameters. [Restrictions:] In all documents and papers that report on research that uses the matlab codes, the researcher(s) must reference the following paper: J. Lu, K.N. Plataniotis, A.N. Venetsanopoulos, S.Z. Li, Ensemble-based Discriminant Learning with Boosting for Face Recognition, IEEE Transactions on Neural Networks, Vol. 17, No. 1, pp. 166-178, January 2006 Any comments and questions can be sent to juwei@dsp.utoronto.ca, tejas@comm.utoronto.ca.

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