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Description: 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.
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Size: 294912 |
Author: 王一 |
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Description: 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 classi cation, etc.).
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Size: 1197056 |
Author: vikas |
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Description:
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Size: 1165312 |
Author: korolis |
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Description: 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
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Size: 1216512 |
Author: 刘大明 |
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Description: 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 classi er s decision boundary
[?] plotData.m- Function to plot 2D classi cation data
[?] sigmoid.m- Sigmoid Function
[?] costFunction.m- Logistic Regression Cost Function
[?] predict.m- Logistic Regression Prediction Function
[?] costFunctionReg.m- Regularized Logistic Regression Cost
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Size: 26624 |
Author: 张伟强 |
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Description: 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 classi er
[?] predictOneVsAll.m- Predict using a one-vs-all multi-class classi er
[?] predict.m- Neural network prediction function
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Size: 7608320 |
Author: 张伟强 |
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