C++實(shí)現(xiàn)isodata聚類算法，基于IRIS數(shù)據(jù)，
上傳時(shí)間： 2016-08-07
上傳用戶：CSUSheep
一種通過(guò)自組織競(jìng)爭(zhēng)學(xué)習(xí)網(wǎng)絡(luò)實(shí)現(xiàn)數(shù)據(jù)降維和可視化的單層神經(jīng)網(wǎng)絡(luò)模型。用此算法可以把輸入空間的多維映射到低維的（一維或者二維）的離散網(wǎng)絡(luò)上，并將保持相同性質(zhì)的輸入數(shù)據(jù)在映射到低維空間時(shí)的拓?fù)湟恢滦浴ris以及l(fā)etter兩個(gè)數(shù)據(jù)集進(jìn)行分類
標(biāo)簽： 網(wǎng)絡(luò) 自組織 數(shù)據(jù) 可視化
上傳時(shí)間： 2016-09-03
上傳用戶：Andy123456
基于遺產(chǎn)算法的FCM算法，且對(duì)iris標(biāo)準(zhǔn)數(shù)據(jù)集聚類，適用初學(xué)者。
上傳時(shí)間： 2013-12-19
上傳用戶：lunshaomo
模式識(shí)別分類器的設(shè)計(jì)，此為K均值法源碼，經(jīng)調(diào)試通過(guò)。所用數(shù)據(jù)為標(biāo)準(zhǔn)IRIS。
上傳時(shí)間： 2014-12-03
上傳用戶：源碼3
模式識(shí)別分類器的設(shè)計(jì)，此為fisher法源碼，經(jīng)調(diào)試通過(guò)。所用數(shù)據(jù)為標(biāo)準(zhǔn)IRIS。
上傳時(shí)間： 2014-01-16
上傳用戶：silenthink
模式識(shí)別分類器的設(shè)計(jì)，此為L(zhǎng)MS法源碼，經(jīng)調(diào)試通過(guò)。所用數(shù)據(jù)為標(biāo)準(zhǔn)IRIS。
上傳時(shí)間： 2014-01-15
上傳用戶：cx111111
K-mean算法，并通過(guò)了IRIS數(shù)據(jù)的測(cè)試。
上傳時(shí)間： 2013-12-18
上傳用戶：shawvi
This approach addresses two difficulties simultaneously: 1) the range limitation of mobile robot sensors and 2) the difficulty of detecting buildings in monocular aerial images. With the suggested method building outlines can be detected faster than the mobile robot can explore the area by itself, giving the robot an ability to “see” around corners. At the same time, the approach can compensate for the absence of elevation data in segmentation of aerial images. Our experiments demonstrate that ground-level semantic information (wall estimates) allows to focus the segmentation of the aerial image to find buildings and produce a ground-level semantic map that covers a larger area than can be built using the onboard sensors.
標(biāo)簽： simultaneously difficulties limitation addresses
上傳時(shí)間： 2014-06-11
上傳用戶：waitingfy
Semantic analysis of multimedia content is an on going research area that has gained a lot of attention over the last few years. Additionally, machine learning techniques are widely used for multimedia analysis with great success. This work presents a combined approach to semantic adaptation of neural network classifiers in multimedia framework. It is based on a fuzzy reasoning engine which is able to evaluate the outputs and the confidence levels of the neural network classifier, using a knowledge base. Improved image segmentation results are obtained, which are used for adaptation of the network classifier, further increasing its ability to provide accurate classification of the specific content.
標(biāo)簽： multimedia Semantic analysis research
上傳時(shí)間： 2016-11-24
上傳用戶：蟲(chóng)蟲(chóng)蟲(chóng)蟲(chóng)蟲(chóng)蟲(chóng)
15篇光流配準(zhǔn)經(jīng)典文獻(xiàn)，目錄如下： 1、A Local Approach for Robust Optical Flow Estimation under Varying 2、A New Method for Computing Optical Flow 3、Accuracy vs. Efficiency Trade-offs in Optical Flow Algorithms 4、all about direct methods 5、An Introduction to OpenCV and Optical Flow 6、Bayesian Real-time Optical Flow 7、Color Optical Flow 8、Computation of Smooth Optical Flow in a Feedback Connected Analog Network 9、Computing optical flow with physical models of brightness Variation 10、Dense estimation and object-based segmentation of the optical flow with robust techniques 11、Example Goal Standard methods Our solution Optical flow under 12、Exploiting Discontinuities in Optical Flow 13、Optical flow for Validating Medical Image Registration 14、Tutorial Computing 2D and 3D Optical Flow.pdf 15、The computation of optical flow
標(biāo)簽： 光流
上傳時(shí)間： 2014-11-21
上傳用戶：fanboynet
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