Mixture modeling is becoming an increasingly important tool in the remote sensing community as researchers attempt to resolve subpixel, area information. This paper compares a well-established technique, linear spectral mixture models (LSMM), with a much newer idea based on data selection, support vector machines (SVM). It is shown that the constrained least squares LSMM is equivalent to the li...

AbstrAct Relevance.In.Co-SVM algorithm, color and texture are naturally considered as sufficient and.uncorrelated.views.of.an.image..SVM classifier is learned in color and texture feature subspaces, respectively. Then the two classifiers are used to classify the unlabeled data. These unlabeled samples that disagree in the two classifiers are chose to label. The extensive experiments show that t...

Establishing and management of social relationships among huge amount users has been provided by the emerging communication medium called online networks (OSNs). The attackers have attracted because rapid increasing OSNs large its subscriber’s personal data. Then they pretend to spread malicious activities, share false news even stolen Twitter is one biggest networking platforms micro blogging ...

The development of the network economy has brought a great impact on society. In process online transactions, if transaction risks are not prevented and improved in time, it will directly affect normal social economy. Therefore, is necessary to synchronize subjects objects, at same time control risks, promote rapid e-commerce, which an important problem be solved urgently current transactions. ...

| Mixture modelling is becoming an increasingly important tool in the remote sensing community as researchers attempt to resolve sub-pixel, area information. This paper compares a well-established technique, Linear Spectral Mixture Models (LSMM), with a much newer idea based on data selection, Support Vector Machines (SVM). It is shown that the constrained least squares LSMM is equivalent to th...