Search - matlab CLASSI

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[matlab] LIBRA_19jun09 DL : 0: Our toolbox currently contains implementations of robust methods for location and scale estimation, covariance estimation (FAST-MCD), regression (FAST- LTS, MCD-regression), principal component analysis (RAPCA, ROBPCA), princi- pal component regression (RPCR), partial least squares (RSIMPLS) and classi¯ cation (RDA). Only a few of these methods will be highlighted in this paper. The toolbox also provides many graphical tools to detect and classify the outliers. The use of these features will be explained and demonstrated through the analysis of some real data sets.
Update : 2025-02-19 Size : 288kb Publisher : 王一
[DSP program] motion DL : 0: This project deals with the tracking and following of single object in a sequence of frames and the velocity of the object is determined. Algorithms are developed for improving the image quality, segmentation, feature extraction and for deterring the velocity. The developed algorithms are implemented and evaluated on TMS320C6416T DSP Starter Kit (DSK). Segmentation is performed to detect the object after reducing the noise from that scene. The object is tracked by plotting a rectangular bounding box around it in each frame. The velocity of the object is determined by calculating the distance that the object moved in a sequence of frames with respect to the frame rate that the video is recorded. The algorithms developed can also be used for other applications (real time, object classication, etc.).
Update : 2025-02-19 Size : 1.14mb Publisher : vikas
[Industry research] Fingerprint DL : 0: Update : 2025-02-19 Size : 1.11mb Publisher : korolis
[Graph Recognize] RSC DL : 0: 强壮的人脸识别系统，发表于cvpr2011年，程序是应用matlab实现-Recently the sparse representation (or coding) based classiﬁ cation (SRC) has been successfully used in face recognition. In SRC, the testing image is represented as a sparse linear combination of the training samples, and the representation ﬁ delity is measured by the � 2-norm or � 1-norm of coding residual. Such a sparse coding model actually assumes that the coding residual follows Gaus- sian or Laplacian distribution, which may not be accurate enough to describe the coding errors in practice. In this paper, we propose a new scheme, namely the robust sparse coding (RSC), by modeling the sparse coding as a sparsity- constrained robust regression problem. The RSC seeks for the MLE (maximum likelihood estimation) solution of the sparse coding problem, and it is much more robust to out- liers (e.g., occlusions, corruptions, etc.) than SRC. An efﬁ cient iteratively reweighted sparse coding algorithm is proposed to solve the RSC model. Extensive
Update : 2025-02-19 Size : 1.16mb Publisher : 刘大明
[Other] mlclass-ex2 DL : 0: 逻辑回归，机器学习相关内容，内有说明，基于matlab仿真-ex2.m- Octave script that will help step you through the exercise ex2 reg.m- Octave script for the later parts of the exercise ex2data1.txt- Training set for the rst half of the exercise ex2data2.txt- Training set for the second half of the exercise submit.m- Submission script that sends your solutions to our servers mapFeature.m- Function to generate polynomial features plotDecisionBounday.m- Function to plot classier s decision boundary [?] plotData.m- Function to plot 2D classication data [?] sigmoid.m- Sigmoid Function [?] costFunction.m- Logistic Regression Cost Function [?] predict.m- Logistic Regression Prediction Function [?] costFunctionReg.m- Regularized Logistic Regression Cost
Update : 2025-02-19 Size : 26kb Publisher : 张伟强
[Other] mlclass-ex3 DL : 0: 多分类学习及神经网络，机器学习相关，基于matlab计算-ex3.m- Octave script that will help step you through part 1 ex3 nn.m- Octave script that will help step you through part 2 ex3data1.mat- Training set of hand-written digits ex3weights.mat- Initial weights for the neural network exercise submitWeb.m- Alternative submission script submit.m- Submission script that sends your solutions to our servers displayData.m- Function to help visualize the dataset fmincg.m- Function minimization routine (similar to fminunc) sigmoid.m- Sigmoid function [?] lrCostFunction.m- Logistic regression cost function [?] oneVsAll.m- Train a one-vs-all multi-class classier [?] predictOneVsAll.m- Predict using a one-vs-all multi-class classier [?] predict.m- Neural network prediction function
Update : 2025-02-19 Size : 7.26mb Publisher : 张伟强