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deboor-cox.rar
- 目的:运用强化学习!多分类器集成!降维方法等最新计算机技术,结合细胞病理知识,设计制作/智能化肺癌细胞病理图像诊断系统0"方法:采集细胞图像,运用基于强化学习的图像分割法将细胞区域从背景中分离出来 运用基于样条和改进2方法对重叠细胞进行分离和重构 提取40个细胞特征用于贝叶斯!支持向量机!紧邻和决策树4种分类器,集成产生肺癌细胞分类结果 建立肺癌细胞病理图库,运用基于等降维方法对细胞进行比对,给予未定型癌细胞分类"结果:/智能化肺癌细胞病理诊断系统0应用于临床随机1200例肺
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- 基于支持向量聚类的多聚焦图像融合算法∗ Exploiting SVC Algorithm for Multifocus Image Fusion-Based on support vector clustering algorithm for multi-focus image fusion
Steganalysis-Based-on-dctdwt-space
- 基于SVM分类器的三域特征融合图像隐写分析算法。三域分别是指dct域，dwt域和空域。-A Steganalysis Based on the Fusion of Three-domain Features of Image and SVM (Support Vector Machines) Classifier.the three domain is the dct,the dwt and the space
knniris
- fusion svm_knn the output of svm will be the input of knn classifier
EmotionClassification
- SPEECH-BASED EMOTION CLASSIFICATION USING MULTICLASS SVM WITH HYBRID KERNEL AND THRESHOLDING FUSION-SPEECH-BASED EMOTION CLASSIFICATION USING MULTICLASS SVM WITH HYBRID KERNEL AND THRESHOLDING FUSION
LBPPLPQFER
- 人脸表情识别matlab程序LBP+LPQ算法融合，SVM分类-facial expression recognition algorithm fusion of LBP and LPQ，clssify by OAA SVM
svm
- Multimodal medical image fusion, as a powerful tool for the clinical applications, has developed with the advent of various imaging modalities in medical imaging.
Fire-detection-multiple-features
- 文章对比分析了火焰不同的形状结构特征，及其特征组合对火焰检测有效性的影响，提出了一种融合圆形度、矩形度和重心高度系数的火焰检测算法，然后将融合后的火焰特征输入支持向量机(SVM)中进行分类- Based on fusion of these flame features，the support vector machine (SVM) is used for classi- fication． Experimental results on the fire videos at Bilken
tqnnnvzi
- 各种kalman滤波器的设计，pwm整流器的建模仿真，包括最小二乘法、SVM、神经网络、1_k近邻法,D-S证据理论数据融合，虚拟力的无线传感网络覆盖，包括广义互相关函数GCC时延估计。-Various kalman filter design, Modeling and simulation pwm rectifier Including the least squares method, the SVM, neural networks, 1 _k neighbor method, D-S
fxnskiec
- LDPC码的完整的编译码，D-S证据理论数据融合，包括最小二乘法、SVM、神经网络、1_k近邻法,给出接收信号眼图及系统仿真误码率，在MATLAB中求图像纹理特征，滤波求和方式实现宽带波束形成。- Complete codec LDPC code, D-S evidence theory data fusion, Including the least squares method, the SVM, neural networks, 1 _k neighbor method, The rece
qqfwaxtt
- D-S证据理论数据融合，GPS和INS组合导航程序，包括最小二乘法、SVM、神经网络、1_k近邻法,包括广义互相关函数GCC时延估计，实现了对10个数字音的识别，基于matlab GUI界面设计。- D-S evidence theory data fusion, GPS and INS navigation program, Including the least squares method, the SVM, neural networks, 1 _k neighbor method, I
hukgtjnh
- 有均匀线阵的CRB曲线，实现了对10个数字音的识别，用于特征降维，特征融合，相关分析等,GPS和INS组合导航程序，BP神经网络用于函数拟合与模式识别，包括最小二乘法、SVM、神经网络、1_k近邻法。- There ULA CRB curve, To achieve the recognition of 10 digital sound, For feature reduction, feature fusion, correlation analysis, GPS and INS naviga
vhktgmrj
- 给出接收信号眼图及系统仿真误码率，D-S证据理论数据融合，LDPC码的完整的编译码，研究生时的现代信号处理的作业，包括最小二乘法、SVM、神经网络、1_k近邻法。- The received signal is given eye and BER simulation systems, D-S evidence theory data fusion, Complete codec LDPC code, Modern signal processing jobs when the graduate
nmtgjscv
- 利用matlab GUI实现的串口编程例子，包括调制，解调，信噪比计算，包括最小二乘法、SVM、神经网络、1_k近邻法,能量熵的计算，D-S证据理论数据融合。- Use serial programming examples matlab GUI implementation, Includes the modulation, demodulation, signal to noise ratio calculation, Including the least squares method,
wmexmqyg
- 单径或多径瑞利衰落信道仿真，从先验概率中采样，计算权重，用于特征降维，特征融合，相关分析等,仿真效率很高的，非归零型差分相位调制信号建模与仿真分析 ，在MATLAB中求图像纹理特征，包括最小二乘法、SVM、神经网络、1_k近邻法。- Single path or multipath Rayleigh fading channel simulation, Sampling a priori probability, calculate the weight, For feature reduct
ckscuqzx
- 进行波形数据分析,matlab小波分析程序，GPS和INS组合导航程序，用于特征降维，特征融合，相关分析等,包括最小二乘法、SVM、神经网络、1_k近邻法,有均匀线阵的CRB曲线，有详细的注释，部分实现了追踪测速迭代松弛算法。- Waveform data analysis, matlab wavelet analysis program, GPS and INS navigation program, For feature reduction, feature fusion, correla
yiqkguqc
- 课程设计时编写的matlab程序代码，包括最小二乘法、SVM、神经网络、1_k近邻法,对于初学matlab的同学会有帮助，有CDF三角函数曲线/三维曲线图，采用热核构造权重，构成不同频率的调制信号，采用波束成形技术的BER计算，D-S证据理论数据融合。- Course designed to prepare the matlab program code, Including the least squares method, the SVM, neural networks, 1 _k nei
ipngnbby
- matlab程序运行时导入数据文件作为输入参数，一种噪声辅助数据分析方法,合成孔径雷达（SAR)目标成像仿真，包括最小二乘法、SVM、神经网络、1_k近邻法,主要为数据分析和统计，D-S证据理论数据融合，搭建OFDM通信系统的框架。- Import data files as input parameters matlab program is running, A noise auxiliary data analysis method, Synthetic Aperture Radar (S
MatlabAUC-master (1)
- As mentioned before, the feature fusion is an efficient step in scene understanding. We proposed to combine between the outputs of CNN algorithm, where the final features can effectively represent the scene images